Evidence Registry

Kücking, F., Hübner, U., Przysucha, M., et al., Automation Bias in AI-Decision Support: Results from an Empirical Study, Studies in Health Technology and Informatics (2024) https://pubmed.ncbi.nlm.nih.gov/39234734/ link

Goddard, K., Roudsari, A., & Wyatt, J. C., Automation bias: a systematic review of frequency, effect mediators, and mitigators, Journal of the American Medical Informatics Association (2012) https://doi.org/10.1136/amiajnl-2011-000089 DOI

Spitzer, P., et al., Don't be Fooled: The Misinformation Effect of Explanations in Human-AI Collaboration (2024) https://arxiv.org/abs/2409.12809 link

"Artificial intelligence-powered social robots for promoting physical activity in older adults: A systematic review." Journal of Sport and Health Science (2025). https://pmc.ncbi.nlm.nih.gov/articles/PMC12272103/ link

Digital Services and Innovation (formerly the Scottish Digital Telecare programme), programme site. https://tec.scot/ link

Intuition Robotics, ElliQ (companion agent for older adults living alone), product site. https://elliq.com/ link

Chan and colleagues, In-Home Positioning for Remote Home Health Monitoring in Older Adults: Systematic Review (JMIR Aging, 2024) https://pmc.ncbi.nlm.nih.gov/articles/PMC11661402/ link

TSA (TEC Services Association C.I.C.), the voice of TEC, sector body site and Analogue to Digital campaign (2026) https://www.tsa-voice.org.uk/ link

An, R., & Lindsey, M. A. (Eds.), Artificial Intelligence in Social Work: Bridging Technology and Humanity (Springer, 2026), Open Access CC BY 4.0 — 24 chapters, each separately keyed by chapter DOI https://doi.org/10.1007/978-3-032-18443-6 DOI

Office of the Inspector General, Los Angeles Board of Police Commissioners (2019). "Review of Selected Los Angeles Police Department Data-Driven Policing Strategies," BPC #19-0072, March 8, 2019. https://www.oig.lacity.org/ link

Detroit Police Department Manual Directive 307.5, Facial Recognition (2024 revision) — a face match is an investigative lead and is NOT a positive identification, requiring corroboration by two examiners and a supervisor https://detroitmi.gov/sites/detroitmi.localhost/files/2024-05/DPD%20REVISION%20307.5%20FACIAL%20RECOGNITION.pdf link

Grother, P., Ngan, M., & Hanaoka, K., Face Recognition Vendor Test (FRVT) Part 3: Demographic Effects, NIST Interagency Report 8280 (December 2019) https://nvlpubs.nist.gov/nistpubs/ir/2019/NIST.IR.8280.pdf link

Williams v. City of Detroit, Stipulated Order of Voluntary Dismissal with Prejudice and Settlement Agreement with Attachments A-E (E.D. Mich., case no. 21-cv-10827, entered 28 June 2024) https://assets.aclu.org/live/uploads/2024/06/Final-Order-of-Dismissal-and-Settlement-Agreement.pdf link

Hunt, P., Saunders, J., & Hollywood, J. S. (2014). "Evaluation of the Shreveport Predictive Policing Experiment." RAND Corporation, RR-531. https://www.rand.org/pubs/research_reports/RR531.html link

Office of the Inspector General, Los Angeles Board of Police Commissioners (2019). "Review of Selected Los Angeles Police Department Data-Driven Policing Strategies," BPC #19-0072, March 8, 2019. http://www.lapdpolicecom.lacity.org/031219/BPC_19-0072.pdf link

Neil, R., & Zanger-Tishler, M. (2025). Algorithmic Bias in Criminal Risk Assessment: The Consequences of Racial Differences in Arrest as a Measure of Crime. Annual Review of Criminology, vol. 8 https://www.annualreviews.org/content/journals/10.1146/annurev-criminol-022422-125019 link

City of Chicago Office of Inspector General, Public Safety Section, Advisory Concerning the Chicago Police Department's Predictive Risk Models (OIG file 18-0106, January 2020) (2020) https://igchicago.org/wp-content/uploads/2020/01/OIG-Advisory-Concerning-CPDs-Predictive-Risk-Models-.pdf link

Saunders, J., Hunt, P., & Hollywood, J. S. (2016). "Predictions put into practice: a quasi-experimental evaluation of Chicago's predictive policing pilot." Journal of Experimental Criminology 12: 347-371. https://link.springer.com/article/10.1007/s11292-016-9272-0 link

Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). "Machine Bias." ProPublica, May 23, 2016; with Larson, J., Mattu, S., Kirchner, L., & Angwin, J. (2016), "How We Analyzed the COMPAS Recidivism Algorithm." https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm link

Dressel, J., & Farid, H. (2018). "The accuracy, fairness, and limits of predicting recidivism." Science Advances 4(1): eaao5580. https://pmc.ncbi.nlm.nih.gov/articles/PMC5777393/ link

State v. Loomis, 2016 WI 68 (Supreme Court of Wisconsin, decided 13 July 2016), case no. 2015AP157-CR, official opinion https://www.wicourts.gov/sc/opinion/DisplayDocument.pdf?content=pdf&seqNo=171690 link

Nearing, G., et al. (2024). "Global prediction of extreme floods in ungauged watersheds." Nature 627: 559-563. https://www.nature.com/articles/s41586-024-07145-1 link

Rainforest Connection (RFCx), programme site. https://rfcx.org/ link

Anticipation Hub (IFRC, German Red Cross and Red Cross Red Crescent Climate Centre), global knowledge hub for anticipatory action https://www.anticipation-hub.org/ link

German Red Cross and partners, "Forecast-based Financing" (programme site). https://www.forecast-based-financing.org/ link

United Nations Office for the Coordination of Humanitarian Affairs, "Anticipatory action" (programme framework page). https://www.unocha.org/anticipatory-action link

Google Research, Flood Hub (operational public flood-alerting interface, sites.research.google/floods, fetched 2026-08-06) https://sites.research.google/floods/ link

World Meteorological Organization, "Early Warnings for All"; and United Nations, "Early Warnings for All". https://www.un.org/en/climatechange/early-warnings-for-all link

World Meteorological Organization, "Early Warnings for All"; and United Nations, "Early Warnings for All". https://wmo.int/activities/early-warnings-all link

ALERTCalifornia (University of California San Diego), About ALERTCalifornia (alertcalifornia.org, 2026) https://alertcalifornia.org/about/ link

ALERTCalifornia (University of California San Diego), programme home page (alertcalifornia.org, 2026) https://alertcalifornia.org/ link

Guevara, M., et al. (2024). "Large language models to identify social determinants of health in electronic health records." npj Digital Medicine 7, 6. https://www.nature.com/articles/s41746-023-00970-0 link

National Academies of Sciences, Engineering, and Medicine (2019). *Integrating Social Care into the Delivery of Health Care: Moving Upstream to Improve the Nation's Health.* Washington, DC: The National Academies Press. https://nap.nationalacademies.org/catalog/25467/integrating-social-care-into-the-delivery-of-health-care link

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). "Dissecting racial bias in an algorithm used to manage the health of populations." Science 366(6464): 447-453. DOI 10.1126/science.aax2342. https://europepmc.org/article/MED/31649194 link

Banerjee, I., Bhimireddy, A.R., Burns, J.L., et al., and Gichoya, J.W. (2021). Reading Race: AI Recognises Patient's Racial Identity In Medical Images. arXiv:2107.10356; published as Gichoya, J.W., et al. (2022), AI recognition of patient race in medical imaging: a modelling study, The Lancet Digital Health, 4(6) https://arxiv.org/abs/2107.10356 link

Centers for Medicare & Medicaid Services. "Accountable Health Communities Model" (CMS Innovation Center model page). https://www.cms.gov/priorities/innovation/innovation-models/ahcm link

Johnson, McPeek Hinz, Regan, Nohria, Moon and Spratt, Measures of Referral vs Receipt of Social Services Among Patients With Health-Related Social Needs (JAMA Network Open, 2024) https://pmc.ncbi.nlm.nih.gov/articles/PMC11024758/ link

Sidebottom, Martins, Vacquier, Dechaine and Behrens, Health-Related Social Needs and Health Care Utilization in the Accountable Health Communities Model (JAMA Network Open, 2025) https://pmc.ncbi.nlm.nih.gov/articles/PMC12706677/ link

Zehner, Kirsch, Adsit, Gorrilla, Hayden, Skora, Rosenblum, Baker, Fiore and McCarthy, Electronic health record closed-loop referral (eReferral) to a state tobacco quitline: a retrospective case study of primary care implementation challenges and adaptations (Implementation Science Communications, 2022) https://pmc.ncbi.nlm.nih.gov/articles/PMC9548147/ link

Hanaoka, K., Ngan, M., Yang, J., Quinn, G. W., Hom, A., & Grother, P. (2024). "Face Analysis Technology Evaluation: Age Estimation and Verification." NIST Interagency Report 8525, May 2024. DOI 10.6028/NIST.IR.8525. https://nvlpubs.nist.gov/nistpubs/ir/2024/NIST.IR.8525.pdf link

Hanaoka, K., Ngan, M., Yang, J., Quinn, G. W., Hom, A., & Grother, P. (2024). "Face Analysis Technology Evaluation: Age Estimation and Verification." NIST Interagency Report 8525, May 2024. DOI 10.6028/NIST.IR.8525. https://pages.nist.gov/frvt/html/frvt_age_estimation.html link

Internet Watch Foundation. https://www.iwf.org.uk/ link

National Center for Missing & Exploited Children, "Take It Down" (minor-focused hash removal service); with the NCMEC service page. https://www.missingkids.org/gethelpnow/isyourexplicitcontentoutthere link

National Center for Missing & Exploited Children, "Take It Down" (minor-focused hash removal service); with the NCMEC service page. https://takeitdown.ncmec.org/ link

StopNCII.org (Revenge Porn Helpline / SWGfL). Service pages: How It Works, Industry Partners, Frequently Asked Questions https://stopncii.org/ link

Thorn, "Safer" (CSAM detection service for platforms). https://safer.io/ link

National Center for Missing & Exploited Children, CyberTipline data. https://www.missingkids.org/cybertiplinedata link

International Labour Organization, Walk Free and International Organization for Migration, Global Estimates of Modern Slavery: Forced Labour and Forced Marriage (2022) https://www.ilo.org/publications/major-publications/global-estimates-modern-slavery-forced-labour-and-forced-marriage link

Marinus Analytics, "Traffic Jam" (investigative triage product page). https://www.marinusanalytics.com/traffic-jam link

Spotlight (escort-advertisement triage tooling), operated by Canary NGO; formerly a Thorn programme. https://spotlight.ngo link

Blomberg, Christensen, Lippert et al., Effect of machine learning on dispatcher recognition of out-of-hospital cardiac arrest during calls to emergency medical services: a randomized clinical trial (JAMA Network Open 2021;4(1):e2032320; Copenhagen EMS) https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2774644 link

Cahoo v. SAS Analytics Inc., Nos. 18-1295/1296 (6th Cir., decided and filed 3 January 2019) — Michigan UIA / MiDAS (Justia PDF and FindLaw reproduction) https://cases.justia.com/federal/appellate-courts/ca6/18-1296/18-1296-2019-01-03.pdf link

Cahoo v. SAS Analytics Inc., Nos. 18-1295/1296 (6th Cir., decided and filed 3 January 2019) — Michigan UIA / MiDAS (Justia PDF and FindLaw reproduction) https://caselaw.findlaw.com/court/us-6th-circuit/1973889.html link

Hernstrom, Sartor, Larsson et al., Interval cancer, sensitivity, and specificity comparing AI-supported mammography screening with standard double reading without AI in the MASAI study: a randomised, controlled, non-inferiority, single-blinded, population-based, screening-accuracy trial. The Lancet (2026, January). doi 10.1016/S0140-6736(25)02464-X https://doi.org/10.1016/S0140-6736(25)02464-X DOI

Lang, Josefsson, Larsson et al., Artificial intelligence-supported screen reading versus standard double reading in the MASAI randomised controlled trial (Lancet Oncology 2023;24(8):936-944) https://pubmed.ncbi.nlm.nih.gov/37541274/ link

Lund University (2026, January 29), AI support in breast cancer screening leads to fewer missed cancer cases (MASAI final results, The Lancet, doi 10.1016/S0140-6736(25)02464-X) https://www.lunduniversity.lu.se/article/ai-support-breast-cancer-screening-fewer-missed-cancer-cases link

NHTSA ODI Recall Query RQ24009 opening resume (25 April 2024), following Recall 23V-838 (Tesla Autosteer driver-engagement remedy, OTA on or shortly after 12 December 2023, software 2023.44.30, 2,031,220 vehicles, estimated percentage with defect 100%) https://static.nhtsa.gov/odi/inv/2024/INOA-RQ24009-12046.pdf link

NTSB Highway Accident Report HAR-19/03, Collision Between Vehicle Controlled by Developmental Automated Driving System and Pedestrian, Tempe, Arizona, March 18, 2018 (Uber ATG) https://www.ntsb.gov/investigations/AccidentReports/Reports/HAR1903.pdf link

SEC Administrative Proceeding File No. 3-15570, Release No. 34-70694, In the Matter of Knight Capital Americas LLC (16 October 2013) https://www.sec.gov/litigation/admin/2013/34-70694.pdf link

Taylor, Jost, MacDonald et al., Evaluation of an ambient AI documentation tool in ambulatory care (JMIR Medical Informatics 2026;14:e86474, doi 10.2196/86474; UC Davis Health) https://medinform.jmir.org/2026/1/e86474/PDF link

Wong, Otles, Donnelly et al., External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients (JAMA Internal Medicine 2021;181(8):1065-1070, doi 10.1001/jamainternmed.2021.2626; PubMed record) https://pubmed.ncbi.nlm.nih.gov/34152373/ link

Wong, Cao, Lyons et al., Quantification of sepsis model alerts in 24 US hospitals before and during the COVID-19 pandemic (JAMA Network Open 2021;4(11):e2135286) https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2786356 link

Goldhaber-Fiebert & Prince (Stanford), Impact evaluation summary: Allegheny Family Screening Tool (Allegheny County DHS, April 2019) https://analytics.alleghenycounty.us/wp-content/uploads/2019/05/Impact-Evaluation-Summary-from-16-ACDHS-26_PredictiveRisk_Package_050119_FINAL-5.pdf link

U.S. Senate Permanent Subcommittee on Investigations (Committee on Homeland Security and Governmental Affairs), Majority Staff Report, Refusal of Recovery: How Medicare Advantage Insurers Have Denied Patients Access to Post-Acute Care, 17 October 2024 https://www.hsgac.senate.gov/wp-content/uploads/2024.10.17-PSI-Majority-Staff-Report-on-Medicare-Advantage.pdf link

Oversight Board (2022, December 6), Policy Advisory Opinion on Meta's cross-check program https://www.oversightboard.com/news/501654971916288-oversight-board-publishes-policy-advisory-opinion-on-meta-s-cross-check-program/ link

Department for Work and Pensions, Universal Credit Advances model fairness assessment (fairness assessment including statistical analysis of the Universal Credit advances machine learning model: 1 April 2024 to 31 March 2025) (2025) https://www.gov.uk/government/publications/fairness-assessment-including-statistical-analysis-of-the-universal-credit-advances-machine-learning-model-1-april-2024-to-31-march-2025/universal-credit-advances-model-fairness-assessment link

Amnesty International, Xenophobic Machines: Discrimination through unregulated use of algorithms in the Dutch childcare benefits scandal (2021) — full-text PDF https://www.amnesty.nl/content/uploads/2021/10/20211014_FINAL_Xenophobic-Machines.pdf link

Ofqual, Awarding GCSE, AS, A level, advanced extension awards and extended project qualifications in summer 2020: interim report (August 2020) https://assets.publishing.service.gov.uk/media/5f3571778fa8f5173f593d61/6656-1_Awarding_GCSE__AS__A_level__advanced_extension_awards_and_extended_project_qualifications_in_summer_2020_-_interim_report.pdf link

Post Office Horizon IT Inquiry, Final Report Volume 1 (July 2025) https://www.postofficehorizoninquiry.org.uk/sites/default/files/2025-07/Post%20Office%20Horizon%20IT%20Inquiry%20Final%20Report%20Volume%201_0.pdf link

The Conversation (2023, July 7), Robodebt royal commissioner makes multiple referrals for prosecution, condemning scheme as 'crude and cruel' https://theconversation.com/robodebt-royal-commissioner-makes-multiple-referrals-for-prosecution-condemning-scheme-as-crude-and-cruel-209318 link

ACLU, Williams v. City of Detroit — face recognition false arrest (case page; settlement 28 June 2024) https://www.aclu.org/cases/williams-v-city-of-detroit-face-recognition-false-arrest link

American Civil Liberties Union, Williams v. City of Detroit settlement one-pager (June 2024) https://assets.aclu.org/live/uploads/2024/06/williams_settlement_one-pager_june_24-1.pdf link

Dentons Data commentary on Moffatt v. Air Canada, 2024 BCCRT 149 (15 February 2024) https://www.dentonsdata.com/airline-ordered-to-compensate-a-b-c-man-because-its-chatbot-provided-inaccurate-information/ link

Federal Court of Australia, Prygodicz v Commonwealth of Australia (No 2) [2021] FCA 634 — the Court's own SUMMARY (Murphy J, 11 June 2021) https://gordonlegal.com.au/app/uploads/2023/08/prygodicz-v-commonwealth-of-australia-no-2-2021-fca-634-summary.pdf link

Google (2025, May 20), SynthID Detector: a portal to identify AI-generated content https://blog.google/innovation-and-ai/products/google-synthid-ai-content-detector/ link

Koenecke, Choi, Mei, Schellmann & Sloane, Careless Whisper: Speech-to-Text Hallucination Harms (ACM FAccT 2024) https://facctconference.org/static/papers24/facct24-111.pdf link

NHTSA (2024, September 30), Consent Order: Cruise LLC crash reporting under the Standing General Order https://www.nhtsa.gov/press-releases/consent-order-cruise-crash-reporting link

US Attorney's Office, N.D. Cal. (2024), Cruise admits submitting false report to influence federal investigation and agrees to pay $500,000 criminal fine https://www.justice.gov/usao-ndca/pr/cruise-admits-submitting-false-report-influence-federal-investigation-and-agrees-pay link

Zillow Group, Q3 2021 shareholder letter (SEC EX-99.3, filed 2 November 2021) https://www.sec.gov/Archives/edgar/data/1617640/000161764021000085/exhibit993.htm link

Commonwealth Ombudsman (Australia), Centrelink's automated debt raising and recovery system (April 2017), via the Internet Archive https://web.archive.org/web/20260715035347if_/https://www.ombudsman.gov.au/__data/assets/pdf_file/0022/43528/Report-Centrelinks-automated-debt-raising-and-recovery-system-April-2017.pdf link

Customer Experience Dive (2025, May 9), Klarna reinvests in human talent for customer service after AI chatbot rollout https://www.customerexperiencedive.com/news/klarna-reinvests-human-talent-customer-service-AI-chatbot/747586/ link

Kusano, Scanlon, Chen, McMurry, Gode & Victor, Comparison of Waymo Rider-Only Crash Data to Human Benchmarks at 56.7 Million Miles (arXiv 2505.01515, 2 May 2025) https://arxiv.org/abs/2505.01515 link

Meta (2020, November 19), Measuring our progress combating hate speech https://about.fb.com/news/2020/11/measuring-progress-combating-hate-speech/ link

The Nevada Independent (2025, July 22), Nevada will use AI for unemployment appeals; some lawmakers are skeptical (DETR / Google) https://thenevadaindependent.com/article/nevada-will-use-ai-for-unemployment-appeals-some-lawmakers-are-skeptical link

Development and Evaluation of a Retrieval-Augmented Generation Chatbot for Orthopedic and Trauma Surgery Patient Education: Mixed-Methods Study (JMIR AI, 23 October 2025, doi 10.2196/75262; BVOU / Orthinform.de) https://pmc.ncbi.nlm.nih.gov/articles/PMC12551339/ link

Hong Kong Lawyer, Pyrrho Investments: the English predictive coding case strikes a new balance in e-discovery (Pyrrho Investments Ltd v MWB Property Ltd [2016] EWHC 256 (Ch), Master Matthews, 16 February 2016) https://www.hk-lawyer.org/content/pyrrho-investments-english-predictive-coding-case-strikes-new-balance-e-discovery link

Kakar, Maiti, Taneja, Nandula, Nguyen, Goel et al., Jill Watson: A Virtual Teaching Assistant powered by ChatGPT (arXiv 2405.11070, 17 May 2024) https://arxiv.org/html/2405.11070v1 link

NautaDutilh, The record fine for the Dutch Tax Administration from a legal perspective (Dutch DPA decision of 7 December 2021, kinderopvangtoeslag risk-classification model) https://www.nautadutilh.com/en/insights/the-record-fine-for-the-dutch-tax-administration-from-a-legal-perspective/ link

Obermeyer, Powers, Vogeli & Mullainathan, Dissecting racial bias in an algorithm used to manage the health of populations (Science 366(6464):447-453, 25 October 2019, doi 10.1126/science.aax2342) https://pubmed.ncbi.nlm.nih.gov/31649194/ link

Powles & Hodson, Google DeepMind and healthcare in an age of algorithms (Health and Technology, 16 March 2017; Royal Free London NHS FT / Streams AKI app) https://pmc.ncbi.nlm.nih.gov/articles/PMC5741783/ link

Ruamviboonsuk et al., Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme (Lancet Digital Health 2022, PubMed 35272972; Google Health / Thailand) https://pubmed.ncbi.nlm.nih.gov/35272972/ link

Braun, Geiger, Amsterdam Fair Welfare AI (Inside Amsterdam's high-stakes experiment to create fair welfare AI) (MIT Technology Review, with Lighthouse Reports and Trouw, 2025) https://www.technologyreview.com/2025/06/11/1118233/amsterdam-fair-welfare-ai-discriminatory-algorithms-failure/ link

Lighthouse Reports, Amsterdam's 'Smart Check' welfare-fraud model: fairness methodology (with Trouw and MIT Technology Review, supported by the Pulitzer Center) https://www.lighthousereports.com/methodology/amsterdam-fairness/ link

Stanford Legal Design Lab / Justice Innovation, How AI is augmenting human-led legal advice at Citizens Advice (Caddy adviser copilot) https://justiceinnovation.law.stanford.edu/how-ai-is-augmenting-human-led-legal-advice-at-citizens-advice/ link

Abramoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018). Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. npj Digital Medicine, 1, 39 https://pmc.ncbi.nlm.nih.gov/articles/PMC6550188/ link

Stapleton et al., Imagining new futures beyond predictive systems in child welfare (FAccT 2022) https://dl.acm.org/doi/10.1145/3531146.3533177 link

Allegheny County DHS, Allegheny Housing Assessment methodology report (2021) https://analytics.alleghenycounty.us/wp-content/uploads/2021/01/20-ACDHS-24-MethodologyReport_01142021_v2.pdf link

Allegheny County DHS, Allegheny Housing Assessment methodology report (2021) https://analytics.alleghenycounty.us/2024/12/18/improving-prioritization-of-housing-services-implementation-of-the-allegheny-housing-assessment/ link

University of Michigan IHPI, What happens when an algorithm cuts your health care https://ihpi.umich.edu/news/what-happens-when-algorithm-cuts-your-health-care link

Upturn, Calculated Need: automated home-care hour allocation https://www.upturn.org/work/calculated-need/ link

Gosciak, J., Giannella, E., Guo, Z., Chen, M., & Koenecke, A. (2026). LLMs in social services: How does chatbot accuracy affect human accuracy? https://arxiv.org/abs/2603.11213 link

U.S. Social Security Administration (agency AI use inventories) https://www.ssa.gov/ link

Kanne, Los Angeles turns to AI to give public benefits enrollment a boost (Route Fifty, 2025) https://www.route-fifty.com/artificial-intelligence/2025/04/los-angeles-turns-ai-give-public-benefits-enrollment-boost/404773/ link

California Legislative Analyst's Office, The 2025-26 Budget: CWS-CARES (2025); California Child Welfare Digital Services (CWS-CARES) https://lao.ca.gov/Publications/Report/5006 link

California Legislative Analyst's Office, The 2025-26 Budget: CWS-CARES (2025); California Child Welfare Digital Services (CWS-CARES) https://cwds.ca.gov/ link

Casenotes and predictive child-welfare models: bias feedback loops (research + ACLU-WA) https://arxiv.org/pdf/2302.08497 link

Casenotes and predictive child-welfare models: bias feedback loops (research + ACLU-WA) https://arxiv.org/pdf/2403.05573 link

Casenotes and predictive child-welfare models: bias feedback loops (research + ACLU-WA) https://www.aclu-wa.org/news/automated-decision-systems-child-welfare-predictive-analytics-tools/ link

The Guardian, DWP algorithm bias by age, disability, marital status, nationality (2024) https://www.theguardian.com/society/2024/dec/06/dwp-algorithm-bias-disabled-people-benefits link

Administration for Children and Families, predictive-analytics child-welfare pilots (2025-2026) https://acf.gov/media/press/2026/acf-announces-6-million-states-pilot-predictive-analytics-child-welfare link

Administration for Children and Families, predictive-analytics child-welfare pilots (2025-2026) https://acf.gov/acyf/policy-guidance/modernizing-child-welfare-technology-predictive-risk-modeling link

The Guardian, Councils using algorithms to make welfare decisions (2019) https://www.theguardian.com/society/2019/oct/16/councils-using-algorithms-make-welfare-decisions-benefits link

Chicago Tribune, Can an algorithm tell when kids are in danger? (2017) https://www.chicagotribune.com/2017/12/06/can-an-algorithm-tell-when-kids-are-in-danger/ link

County of Los Angeles Department of Children and Family Services, Fact Sheet FY 2024-2025 (2025) https://dcfs.lacounty.gov/wp-content/uploads/2025/10/Factsheet-FY-2024-2025.pdf link

Beam, Magic Notes (assessment transcription/summarization) https://www.beam.org/magic-notes link

Unity Insights, Magic Notes Validation Report v3.0 — independent validation of Beam's Kent County Council evaluation, NICE Evidence Standards Framework (2025) https://unityinsights.co.uk/wp-content/uploads/2025/10/202509_UI-Magic-Notes-Validation-Report-v3.0.pdf link

IEEE Spectrum, Michigan's MiDAS unemployment system: Algorithm alchemy that created lead, not gold https://spectrum.ieee.org/michigans-midas-unemployment-system-algorithm-alchemy-that-created-lead-not-gold link

UK Government, M365 Copilot and data protection https://www.gov.uk/government/publications/m365-copilot-and-data-protection link

Nevada generative-AI unemployment-appeals RAG (Route Fifty; GovTech; Nevada Independent) https://www.govtech.com/artificial-intelligence/nevada-harnesses-genai-for-employment-claims-evaluation link

Nevada generative-AI unemployment-appeals RAG (Route Fifty; GovTech; Nevada Independent) https://thenevadaindependent.com/article/opinion-wrong-answers-faster-meet-nevadas-new-unemployment-ai-overlord link

Nevada generative-AI unemployment-appeals RAG (Route Fifty; GovTech; Nevada Independent) https://www.route-fifty.com/artificial-intelligence/2025/05/nevada-turns-ai-speed-unemployment-appeals/404987/ link

OECD.AI Incidents Monitor, NYC MyCity Chatbot Gives Dangerous, Illegal Advice to Businesses (2024) https://oecd.ai/en/incidents/2024-03-29-3dce link

The Markup, NYC's AI chatbot tells businesses to break the law (2024); OECD AI incident https://themarkup.org/artificial-intelligence/2024/03/29/nycs-ai-chatbot-tells-businesses-to-break-the-law link

Prygodicz v Commonwealth of Australia (No 2) [2021] FCA 634 (Federal Court of Australia) https://robodebt.royalcommission.gov.au/publications/exhibit-2-2598-rbd999900010225-prygodicz-v-commonwealth-australia-no-2-2021-fca-634 link

Royal Commission into the Robodebt Scheme (2023) https://robodebt.royalcommission.gov.au/ link

Rekenkamer Rotterdam, Gekleurde technologie: onderzoek naar het gebruik van algoritmes door de gemeente Rotterdam (2021) https://www.rekenkamers.nl/rapport/gekleurde-technologie/ link

WIRED / Lighthouse Reports, Inside the suspicion machine (2023) https://www.wired.com/story/welfare-state-algorithms/ link

Amnesty International, Xenophobic machines: Discrimination through unregulated use of algorithms in the Dutch childcare benefits scandal (2021) https://www.amnesty.org/en/documents/eur35/4686/2021/en/ link

KFF Health News (Rachana Pradhan and Samantha Liss), Medicaid for Millions in America Hinges on Deloitte-Run Systems Plagued by Errors (2024) https://kffhealthnews.org/news/article/medicaid-deloitte-run-eligibility-systems-plagued-by-errors/ link

Connecticut DSS, CT DSS Self-Service Chatbot (Laurel) knowledge-base article https://portal.ct.gov/dss/knowledge-base/articles/ct-dss-self-service-chatbot link

Vaithianathan, Benavides-Prado, Rebbe & Putnam-Hornstein, Using a Predictive Risk Model to Prioritize Families for Prevention Services: The Hello Baby Program in Allegheny County, PA, Prevention Science (2025) https://pmc.ncbi.nlm.nih.gov/articles/PMC12064473/ link

WitnessLA (Richard Wexler), LA County Nixes Alarmingly Unreliable Predictive Analytics Foster Care Scheme - For Now (2017) https://witnessla.com/op-ed-la-county-nixes-alarming-predictive-analytics-scheme-for-foster-care-for-now/ link

FDA, Artificial Intelligence-Enabled Medical Devices (official device list) https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-enabled-medical-devices link

Wong, A., Otles, E., et al. (2021). External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients. JAMA Internal Medicine https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2781307 link

Adams, R., Henry, K.E., et al. (2022). Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis. Nature Medicine, 28(7), 1455-1460. https://doi.org/10.1038/s41591-022-01894-0 https://www.nature.com/articles/s41591-022-01894-0 DOI

Ancker, J.S., Edwards, A., Nosal, S., Hauser, D., Mauer, E., & Kaushal, R. (2017). Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC Medical Informatics and Decision Making, 17, 36. https://doi.org/10.1186/s12911-017-0430-8 https://link.springer.com/article/10.1186/s12911-017-0430-8 DOI

Goddard, K., Roudsari, A., & Wyatt, J.C. (2012). Automation bias: a systematic review of frequency, effect mediators, and mitigators. Journal of the American Medical Informatics Association, 19(1), 121-127. https://academic.oup.com/jamia/article-abstract/19/1/121/732254 link

Henry, K.E., et al. (2022). Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing. Nature Medicine, 28. https://doi.org/10.1038/s41591-022-01895-z https://www.nature.com/articles/s41591-022-01895-z DOI

Lim, Y.S., et al. (2025). Non-Contrast Computed Tomography-Based Triage and Notification for Large Vessel Occlusion Stroke: A Before and After Study Utilizing Artificial Intelligence on Treatment Times and Outcomes. Journal of Clinical Medicine, 14(4), 1281. https://doi.org/10.3390/jcm14041281 https://pmc.ncbi.nlm.nih.gov/articles/PMC11856584/ DOI

McCoy, A.B., et al. (2022). Clinician collaboration to improve clinical decision support: the Clickbusters initiative. Journal of the American Medical Informatics Association, 29(6), 1050-1059. https://academic.oup.com/jamia/article/29/6/1050/6542395 link

Poly, T.N., et al. (2020). Appropriateness of Overridden Alerts in Computerized Physician Order Entry: Systematic Review. JMIR Medical Informatics, 8(7), e15653. https://doi.org/10.2196/15653 https://medinform.jmir.org/2020/7/e15653 DOI

Sendak, M.P., et al. (2020). Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study. JMIR Medical Informatics, 8(7), e15182. https://doi.org/10.2196/15182 https://medinform.jmir.org/2020/7/e15182/ DOI

U.S. Food and Drug Administration (2022). Clinical Decision Support Software: Guidance for Industry and FDA Staff (final guidance). https://www.federalregister.gov/documents/2022/09/28/2022-20993/clinical-decision-support-software-guidance-for-industry-and-food-and-drug-administration-staff link

Williams, B. (2024). The National Early Warning Score: from concept to NHS implementation. Clinical Medicine (Royal College of Physicians). https://www.sciencedirect.com/science/article/pii/S147021182402623X link

Williams, B. (2024). The National Early Warning Score: from concept to NHS implementation. Clinical Medicine (Royal College of Physicians). https://www.rcplondon.ac.uk/projects/outputs/national-early-warning-score-news-2 link

Wong, A., Currey, D., Schwinne, M., et al. (2026). Multicenter Prospective Validation of an Updated Proprietary Sepsis Prediction Model. JAMA Network Open. https://doi.org/10.1001/jamanetworkopen.2026.0181 https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2845595 DOI

Elish, M.C., & Watkins, E.A. (2020). Repairing Innovation: A Study of Integrating AI in Clinical Care. Data & Society Research Institute. https://datasociety.net/library/repairing-innovation/ link

Felisberto, M., dos Santos Lima, G., Celuppi, I.C., et al. (2024). Override rate of drug-drug interaction alerts in clinical decision support systems: A brief systematic review and meta-analysis. Health Informatics Journal. https://doi.org/10.1177/14604582241263242 https://journals.sagepub.com/doi/10.1177/14604582241263242 DOI

STAT News (2021, July 26). Epic's AI algorithms, shielded from scrutiny by a corporate firewall, are delivering inaccurate information on seriously ill patients. https://www.statnews.com/2021/07/26/epic-hospital-algorithms-sepsis-investigation/ link

STAT News (2022, Oct 3). Epic overhauls popular sepsis algorithm criticized for faulty alarms. https://www.statnews.com/2022/10/03/epic-sepsis-algorithm-revamp-training/ link

Escobar, G.J., Liu, V.X., Schuler, A., Lawson, B., Greene, J.D., & Kipnis, P. (2020). Automated Identification of Adults at Risk for In-Hospital Clinical Deterioration. New England Journal of Medicine, 383(20), 1951-1960. https://doi.org/10.1056/NEJMsa2001090 https://www.nejm.org/doi/full/10.1056/NEJMsa2001090 DOI

The Kaiser Permanente Northern California Advance Alert Monitor Program: An Automated Early Warning System for Adults at Risk for In-Hospital Clinical Deterioration (2022). Joint Commission Journal on Quality and Patient Safety. https://www.jointcommissionjournal.com/article/S1553-7250(22)00110-6/fulltext link

U.S. Food and Drug Administration (2018, February 13). FDA permits marketing of clinical decision support software for alerting providers of a potential stroke in patients (press announcement) https://www.fda.gov/news-events/press-announcements/fda-permits-marketing-clinical-decision-support-software-alerting-providers-potential-stroke link

Afshar, M., Baumann, M.R., Resnik, F., et al. (2025). A Pragmatic Randomized Controlled Trial of Ambient Artificial Intelligence to Improve Health Practitioner Well-Being. NEJM AI. https://doi.org/10.1056/AIoa2500945 https://pubmed.ncbi.nlm.nih.gov/41625485/ DOI

Burke, G., & Schellmann, H. / Associated Press (2024, Oct 26). Researchers say an AI-powered transcription tool used in hospitals invents things no one ever said. https://www.wusf.org/economy-business/2024-11-02/researchers-ai-powered-hospital-transcription-tool-invents-things-no-one-said link

Chen, S., et al. (2024). The effect of using a large language model to respond to patient messages. Lancet Digital Health, 6(6). https://doi.org/10.1016/S2589-7500(24)00060-8 https://www.thelancet.com/journals/landig/article/PIIS2589-7500(24)00060-8/fulltext DOI

Dai, T., Kvedar, J.C., & Polsky, D. (2025). Policy brief: ambient AI scribes and the coding arms race. npj Digital Medicine. https://doi.org/10.1038/s41746-025-02272-z https://www.nature.com/articles/s41746-025-02272-z DOI

Genes, N., Sills, J., Heaton, H.A., Shy, B.D., & Scofi, J. (2025). Addressing Note Bloat: Solutions for Effective Clinical Documentation. Journal of the American College of Emergency Physicians Open. https://doi.org/10.1016/j.acepjo.2024.100031 https://pmc.ncbi.nlm.nih.gov/articles/PMC11852943/ DOI

Koenecke, A., Choi, A.S.G., Mei, K.X., Schellmann, H., & Sloane, M. (2024). Careless Whisper: Speech-to-Text Hallucination Harms. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT '24). https://doi.org/10.1145/3630106.3658996 https://dl.acm.org/doi/10.1145/3630106.3658996 DOI

Nong, P., et al. (2026). Unintended Consequences of Using Ambient Artificial Intelligence Scribes for Billing. JAMA Health Forum, 7(1), e255771. https://doi.org/10.1001/jamahealthforum.2025.5771 https://jamanetwork.com/journals/jama-health-forum/fullarticle/2843723 DOI

Palm, K.H., Manikantan, K., Mahal, N., Belwadi, S.K., & Pepin, R.J. (2025). Assessing the quality of AI-generated clinical notes: validated evaluation of a large language model ambient scribe. Frontiers in Artificial Intelligence, 8. https://doi.org/10.3389/frai.2025.1691499 https://pmc.ncbi.nlm.nih.gov/articles/PMC12586549/ DOI

Peterson Health Technology Institute (2025). Adoption of Artificial Intelligence in Healthcare Delivery Systems: Early Applications and Impacts. PHTI AI Taskforce. https://phti.org/wp-content/uploads/sites/3/2025/03/PHTI-Adoption-of-AI-in-Healthcare-Delivery-Systems-Early-Applications-Impacts.pdf link

Rotenstein, L.S., et al. (2026). Changes in Clinician Time Expenditure and Visit Quantity With Adoption of Artificial Intelligence-Powered Scribes: A Multisite Study. JAMA. https://doi.org/10.1001/jama.2026.2253 https://pubmed.ncbi.nlm.nih.gov/41920565/ DOI

Stults, C.D., Deng, S., Martinez, M.C., et al. (2025). Evaluation of an Ambient Artificial Intelligence Documentation Platform for Clinicians. JAMA Network Open, 8(5), e258614. https://doi.org/10.1001/jamanetworkopen.2025.8614 https://pubmed.ncbi.nlm.nih.gov/40314951/ DOI

Tai-Seale, M., Baxter, S.L., Vaida, F., et al. (2024). AI-Generated Draft Replies Integrated Into Health Records and Physicians' Electronic Communication. JAMA Network Open, 7(4), e246565. https://doi.org/10.1001/jamanetworkopen.2024.6565 https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2817615 DOI

You, J.G., Dbouk, R.H., Landman, A., et al. (2025). Ambient Documentation Technology in Clinician Experience of Documentation Burden and Burnout. JAMA Network Open, 8(8), e2528056. https://doi.org/10.1001/jamanetworkopen.2025.28056 https://pubmed.ncbi.nlm.nih.gov/40839265/ DOI

American Health Information Management Association (AHIMA). Governance and AI: Putting Rules in Place is Critical (accessed 2026-08-06) https://www.ahima.org/education-events/artificial-intelligence/governance-and-ai-putting-rules-in-place-is-critical/ link

Tierney, A.A., et al. (2025). Ambient Artificial Intelligence Scribes: Learnings after 1 Year and over 2.5 Million Uses. NEJM Catalyst Innovations in Care Delivery. https://doi.org/10.1056/CAT.25.0040 https://divisionofresearch.kaiserpermanente.org/ai-assisted-notetaking-gains-steady-support-from-kaiser-permanente-physicians/ DOI

Tierney, A.A., Gayre, G., Hoberman, B., et al. (2024). Ambient Artificial Intelligence Scribes to Alleviate the Burden of Clinical Documentation. NEJM Catalyst Innovations in Care Delivery. https://doi.org/10.1056/CAT.23.0404 https://catalyst.nejm.org/doi/full/10.1056/CAT.23.0404 DOI

Afshar, M., et al. (2025). A Novel Playbook for Pragmatic Trial Operations to Monitor and Evaluate Ambient Artificial Intelligence in Clinical Practice. NEJM AI. https://doi.org/10.1056/AIdbp2401267 https://ai.nejm.org/doi/full/10.1056/AIdbp2401267 DOI

Beckett, C., & Yaseen, M. (2023). Generating Change: A global survey of what news organisations are doing with AI. JournalismAI / POLIS, LSE. https://www.journalismai.info/research/2023-generating-change link

Cambridge Consultants (2019). Use of AI in Online Content Moderation. Report for Ofcom. https://www.ofcom.org.uk/__data/assets/pdf_file/0028/157249/cambridge-consultants-ai-content-moderation.pdf link

Diakopoulos, N., Cools, H., Li, C., Helberger, N., Kung, E., & Rinehart, A. (2024, April). Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem. Associated Press / Northwestern University. https://www.aim4dem.nl/wp-content/uploads/2024/04/AP_Generative_AI_Report_April_202426-1.pdf link

Gorwa, R., Binns, R., & Katzenbach, C. (2020). Algorithmic content moderation: Technical and political challenges in the automation of platform governance. Big Data & Society, 7(1). https://doi.org/10.1177/2053951719897945 https://ora.ox.ac.uk/objects/uuid:5f9e41fb-d96a-4230-a41e-b125eba2519b DOI

Oversight Board (2025, August 27). 2024 Annual Report: Highlights Board's Impact in the Year of Elections. https://www.oversightboard.com/news/2024-annual-report-highlights-boards-impact-in-the-year-of-elections/ link

Simon, F.M. (2024, February 6). Artificial Intelligence in the News: How AI Retools, Rationalizes, and Reshapes Journalism and the Public Arena. Tow Center for Digital Journalism, Columbia University. https://www.cjr.org/tow_center_reports/artificial-intelligence-in-the-news.php link

Bonifacic, I. (2023, January 25). CNET had to correct most of its AI-written articles. Engadget. https://www.engadget.com/cnet-corrected-41-of-its-77-ai-written-articles-201519489.html link

Harrison Dupré, M. (2023, November 27). Sports Illustrated Published Articles by Fake, AI-Generated Writers. Futurism. https://futurism.com/sports-illustrated-ai-generated-writers link

Human Rights Watch (2020, September 10). 'Video Unavailable': Social Media Platforms Remove Evidence of War Crimes. https://www.hrw.org/report/2020/09/10/video-unavailable/social-media-platforms-remove-evidence-war-crimes link

Oversight Board (2024, June 27). 2023 Annual Report Shows Board's Impact on Meta. https://www.oversightboard.com/news/2023-annual-report-shows-boards-impact-on-meta/ link

Steen, E., Yurechko, K., & Klug, D. (2023). You Can (Not) Say What You Want: Using Algospeak to Contest and Evade Algorithmic Content Moderation on TikTok. Social Media + Society, 9(3). https://doi.org/10.1177/20563051231194586 https://journals.sagepub.com/doi/10.1177/20563051231194586 DOI

Kaplan, J. (2025, January 7). More Speech and Fewer Mistakes. Meta Newsroom. https://about.fb.com/news/2025/01/meta-more-speech-fewer-mistakes/ link

Meta Platforms (quarterly). Community Standards Enforcement Report. Meta Transparency Center. https://transparency.meta.com/reports/community-standards-enforcement/ link

Meta Platforms (2025, May 29). Integrity Reports, First Quarter 2025. Meta Transparency Center. https://transparency.meta.com/reports/integrity-reports-q1-2025/ link

Allyn, B. (2020, May 12). In Settlement, Facebook To Pay $52 Million To Content Moderators With PTSD. NPR (Scola v. Facebook). https://www.npr.org/2020/05/12/854998616/in-settlement-facebook-to-pay-52-million-to-content-moderators-with-ptsd link

Newton, C. (2019, February 25). The Trauma Floor: The secret lives of Facebook moderators in America. The Verge. https://www.theverge.com/2019/2/25/18229714/cognizant-facebook-content-moderator-interviews-trauma-working-conditions-arizona link

Steiger, M., Bharucha, T.J., Venkatagiri, S., Riedl, M.J., & Lease, M. (2021). The Psychological Well-Being of Content Moderators. In Proceedings of CHI '21. https://doi.org/10.1145/3411764.3445092 https://crowd.cs.vt.edu/wp-content/uploads/2021/02/CHI21_final__The_Psychological_Well_Being_of_Content_Moderators-2.pdf DOI

YouTube / Google (2020, August 25). Responsible policy enforcement during Covid-19. Official YouTube blog. https://blog.youtube/inside-youtube/responsible-policy-enforcement-during-covid-19/ link

HHS / SAMHSA, SAMHSA Awards $255 Million to Administer 988 Lifeline https://www.hhs.gov/press-room/samhsa-awards-255-million-to-administer-988-lifeline.html link

Eysenbach, Crisis Text Line and Loris.ai Controversy Highlights the Complexity of Informed Consent on the Internet and Data-Sharing Ethics for Machine Learning and Research (Journal of Medical Internet Research, editorial, 2025) https://pmc.ncbi.nlm.nih.gov/articles/PMC11799832/ link

AI Incident Database, Incident 1039 (April 2025). Anysphere AI Support Bot for Cursor Reportedly Invents Login Policy, Leading to Subscription Cancellations. https://incidentdatabase.ai/cite/1039/ link

Aksin, Z., Armony, M., & Mehrotra, V. (2007). The Modern Call Center: A Multi-Disciplinary Perspective on Operations Management Research. Production and Operations Management, 16(6), 665-688. https://doi.org/10.1111/j.1937-5956.2007.tb00288.x https://journals.sagepub.com/doi/abs/10.1111/j.1937-5956.2007.tb00288.x DOI

Bernhardt, A., Kresge, L., & Suleiman, R. (2021). Data and Algorithms at Work: The Case for Worker Technology Rights. UC Berkeley Labor Center. https://laborcenter.berkeley.edu/wp-content/uploads/2021/11/Data-and-Algorithms-at-Work.pdf link

Burleigh, E. (2024, June 11). Amazon customer service agents are scared of being replaced by AI. Fortune. https://fortune.com/2024/06/11/amazon-customer-service-agents-scared-ai-replacement/ link

Communications Workers of America (2023-2024). CWA Resource Hub on Artificial Intelligence (including the call-center AI survey factsheet with Cornell/McMaster). https://cwa-union.org/workers-rights/artificial-intelligence link

Doellgast, V., O'Brady, S., Kim, J., Walters, D., et al. (2023). AI in Contact Centers: Artificial Intelligence and Algorithmic Management in Frontline Service Workplaces. Cornell University ILR School (refereed companion: Doellgast, Wagner & O'Brady, Transfer 29(1), 2023, https://doi.org/10.1177/10242589221143044). https://ecommons.cornell.edu/entities/publication/2c04c957-d672-400e-9aed-adb5f6ace640 DOI

Gartner, Inc. (2024, July 9). Gartner Survey Finds 64% of Customers Would Prefer That Companies Didn't Use AI For Customer Service (survey of 5,728 customers). https://www.gartner.com/en/newsroom/press-releases/2024-07-09-gartner-survey-finds-64-percent-of-customers-would-prefer-that-companies-didnt-use-ai-for-customer-service link

Gartner, Inc. (2024, July 9). Gartner Survey Finds 64% of Customers Would Prefer That Companies Didn't Use AI For Customer Service (survey of 5,728 customers). https://www.theregister.com/2024/07/09/gartner_simply_replacing_hold_music/ link

Gartner, Inc. (2025, June 10). Gartner Predicts 50% of Organizations Will Abandon Plans to Reduce Customer Service Workforce Due to AI (poll of 163 service leaders). https://www.gartner.com/en/newsroom/press-releases/2025-06-10-gartner-predicts-50-percent-of-organizations-will-abandon-plans-to-reduce-customer-service-workforce-due-to-ai link

Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases. Marketing Science, 38(6), 937-947. https://doi.org/10.1287/mksc.2019.1192 https://www.fox.temple.edu/sites/fox/files/Frontiers-Machines-versus-Humans-The-Impact-of-Artificial-Intelligence-Chatbot-Disclosure-on-Customer-Purchases.pdf DOI

The New York Times (2023, July). 'Training My Replacement': Inside a Call Center Worker's Battle With A.I. (republished by The Seattle Times). https://www.seattletimes.com/explore/careers/training-my-replacement-inside-a-call-center-workers-battle-with-ai/ link

Moffatt v. Air Canada, 2024 BCCRT 149 (British Columbia Civil Resolution Tribunal, February 14, 2024). https://www.canlii.org/en/bc/bccrt/doc/2024/2024bccrt149/2024bccrt149.html link

Sookman, B.B. (2024, February 19). Moffatt v. Air Canada: A Misrepresentation by an AI Chatbot. McCarthy Tétrault TechLex blog https://www.mccarthy.ca/en/insights/blogs/techlex/moffatt-v-air-canada-misrepresentation-ai-chatbot link

ITV News (2024, January 19). DPD disables AI chatbot after customer service bot appears to go rogue. https://www.itv.com/news/2024-01-19/dpd-disables-ai-chatbot-after-customer-service-bot-appears-to-go-rogue link

Gartner, Inc. (2025, June 10). Gartner Predicts 50% of Organizations Will Abandon Plans to Reduce Customer Service Workforce Due to AI (poll of 163 service leaders). https://www.theregister.com/software/2025/06/11/half_of_firms_set_to_abandon_plans_to_ditch_customer_service/502135 link

Ivanova, I. (2025, May 9). Klarna plans to hire humans again, as new landmark survey reveals most AI projects fail to deliver. Fortune. https://fortune.com/2025/05/09/klarna-ai-humans-return-on-investment/ link

Klarna Bank AB (2024, February 27). Klarna AI assistant handles two-thirds of customer service chats in its first month (press release via PR Newswire). https://www.prnewswire.com/news-releases/klarna-ai-assistant-handles-two-thirds-of-customer-service-chats-in-its-first-month-302072740.html link

Brynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at Work. The Quarterly Journal of Economics, 140(2), 889-942. https://doi.org/10.1093/qje/qjae044 https://academic.oup.com/qje/article/140/2/889/7990658 DOI

Stanford RegLab, Artificial Intelligence for Adjudication: The Social Security Administration and AI Governance (publication page) (2022) https://reglab.stanford.edu/publications/artificial-intelligence-for-adjudication-the-social-security-administration-and-ai-governance/ link

Feathers, T. (2023, April 27). False Alarm: How Wisconsin Uses Race and Income to Label Students 'High Risk'. The Markup (with Chalkbeat). https://themarkup.org/machine-learning/2023/04/27/false-alarm-how-wisconsin-uses-race-and-income-to-label-students-high-risk link

NPR, Justice Department works to curb racial bias in deciding who's released from prison (2022) https://www.npr.org/2022/04/19/1093538706/justice-department-works-to-curb-racial-bias-in-deciding-whos-released-from-pris link

Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine Bias. ProPublica, 23 May 2016; with Larson, J., Mattu, S., Kirchner, L., & Angwin, J. (2016), How We Analyzed the COMPAS Recidivism Algorithm https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing link

Invisible staffing churn in nursing homes: CMS turnover metrics miss a growing short-term workforce, Health Affairs Scholar (2026) https://academic.oup.com/healthaffairsscholar/article/4/5/qxag094/8658564 link

GOV.UK, Government-built Humphrey AI tool reviews responses to consultation for first time (2025) https://www.gov.uk/government/news/government-built-humphrey-ai-tool-reviews-responses-to-consultation-for-first-time-in-bid-to-save-millions link

IRCC (Government of Canada), CIMM Question Period Note: Use of AI in Decision-Making at IRCC (29 Nov 2022) https://www.canada.ca/en/immigration-refugees-citizenship/corporate/transparency/committees/cimm-nov-29-2022/question-period-note-use-ai-decision-making-ircc.html link

CBS News, UnitedHealth uses faulty AI to deny elderly patients medically necessary coverage, lawsuit claims https://www.cbsnews.com/news/unitedhealth-lawsuit-ai-deny-claims-medicare-advantage-health-insurance-denials/ link

Rucker, P., Miller, M., & Armstrong, D. (2023, March 25). How Cigna Saves Millions by Having Its Doctors Reject Claims Without Reading Them. ProPublica, co-published with The Capitol Forum https://www.propublica.org/article/cigna-pxdx-medical-health-insurance-rejection-claims link

Bogen, M., & Rieke, A. (2018). Help Wanted: An Examination of Hiring Algorithms, Equity, and Bias. Upturn. https://www.upturn.org/static/reports/2018/hiring-algorithms/files/Upturn%20--%20Help%20Wanted%20-%20An%20Exploration%20of%20Hiring%20Algorithms,%20Equity%20and%20Bias.pdf link

Cowgill, B. (2018). Bias and Productivity in Humans and Algorithms: Theory and Evidence from Résumé Screening. Columbia Business School working paper (SSRN 3433737). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3433737 link

Fuller, J.B., Raman, M., Sage-Gavin, E., & Hines, K. (2021). Hidden Workers: Untapped Talent. Harvard Business School Project on Managing the Future of Work & Accenture. https://www.hbs.edu/managing-the-future-of-work/research/Pages/hidden-workers-untapped-talent.aspx link

Hoffman, M., Kahn, L.B., & Li, D. (2018). Discretion in Hiring. Quarterly Journal of Economics, 133(2), 765-800. https://doi.org/10.1093/qje/qjx042 https://www.nber.org/papers/w21709 DOI

Li, D., Raymond, L.R., & Bergman, P. (2020). Hiring as Exploration. NBER Working Paper 27736. https://doi.org/10.3386/w27736 https://www.nber.org/papers/w27736 DOI

Maurer, R. (2021). HireVue Discontinues Facial Analysis Screening. SHRM; with HireVue and ORCAA audit announcements (2021). https://orcaarisk.com/in-the-news/2021/1/12/orcaas-audit-of-hirevue-is-live link

New York City Local Law 144 of 2021 (Automated Employment Decision Tools); NYC Department of Consumer and Worker Protection, Final Rules (2023). https://rules.cityofnewyork.us/rule/automated-employment-decision-tools-updated/ link

New York City Local Law 144 of 2021 (Automated Employment Decision Tools); NYC Department of Consumer and Worker Protection, Final Rules (2023). https://www.nyc.gov/assets/dca/downloads/pdf/about/DCWP-AEDT-FAQ.pdf link

New York State Office of the State Comptroller (2025, December 2). Enforcement of Local Law 144 - Automated Employment Decision Tools. Audit Report 2024-N-6. https://www.osc.ny.gov/state-agencies/audits/2025/12/02/enforcement-local-law-144-automated-employment-decision-tools link

Wilson, C., Ghosh, A., Jiang, S., Mislove, A., Baker, L., Szary, J., Trindel, K., & Polli, F. (2021). Building and Auditing Fair Algorithms: A Case Study in Candidate Screening. In Proceedings of FAccT '21, 666-677. https://doi.org/10.1145/3442188.3445928 https://www.ccs.neu.edu/home/amislove/publications/Pymetrics-FAccT.pdf DOI

Wilson, K., & Caliskan, A. (2024). Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES 2024), 7(1), 1578-1590. https://doi.org/10.48550/arXiv.2407.20371 https://ojs.aaai.org/index.php/AIES/article/view/31748 DOI

Wright, L., Muenster, R.M., Vecchione, B., et al. (2024). Null Compliance: NYC Local Law 144 and the Challenges of Algorithm Accountability. In Proceedings of FAccT '24, 1701-1713. https://doi.org/10.1145/3630106.3658998 https://arxiv.org/abs/2406.01399 DOI

Dastin, J. (2018, October 10). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https://www.euronews.com/business/2018/10/10/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women link

Maurer, R. (2021). HireVue Discontinues Facial Analysis Screening. SHRM; with HireVue and ORCAA audit announcements (2021). https://www.shrm.org/topics-tools/news/talent-acquisition/hirevue-discontinues-facial-analysis-screening link

Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020). Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices. In Proceedings of FAT* '20, 469-481. https://doi.org/10.1145/3351095.3372828 https://arxiv.org/abs/1906.09208 DOI

ACLU, ACLU of Colorado, Public Justice & Eisenberg & Baum LLP (2025, March). Civil-rights charges over an automated HireVue video interview at Intuit (Deaf and Indigenous applicant); via HR Dive and Fisher Phillips coverage. https://www.hrdive.com/news/ai-intuit-hirevue-deaf-indigenous-employee-discrimination-aclu/743273/ link

ACLU, ACLU of Colorado, Public Justice & Eisenberg & Baum LLP (2025, March). Civil-rights charges over an automated HireVue video interview at Intuit (Deaf and Indigenous applicant); via HR Dive and Fisher Phillips coverage. https://www.fisherphillips.com/en/insights/insights/ai-screening-systems-face-fresh-scrutiny-6-key-takeaways-from-claims-filed-against-hiring-technology-company link

U.S. Equal Employment Opportunity Commission (2023, September 11). iTutorGroup to Pay $365,000 to Settle EEOC Discriminatory Hiring Suit (EEOC v. iTutorGroup, No. 1:22-cv-02565, E.D.N.Y.). https://www.eeoc.gov/newsroom/itutorgroup-pay-365000-settle-eeoc-discriminatory-hiring-suit link

Best Practice AI. Unilever saved over 50,000 hours in candidate interview time and delivered over £1M annual savings and improved candidate diversity with machine analysis of video-based interviewing (AI case study). https://www.bestpractice.ai/ai-case-study-best-practice/unilever_saved_over_50,000_hours_in_candidate_interview_time_and_delivered_over_%C2%A31m_annual_savings_and_improved_candidate_diversity_with_machine_analysis_of_video-based_interviewing. link

Mobley v. Workday, Inc., No. 3:23-cv-00770 (N.D. Cal.): agent-theory vendor liability (2024), preliminary nationwide ADEA collective certification (2025), bias-testing privilege ruling (2026); via Holland & Knight LLP analysis. https://www.hklaw.com/en/insights/publications/2025/05/federal-court-allows-collective-action-lawsuit-over-alleged link

U.S. EEOC (2023, May 18). Select Issues: Assessing Adverse Impact in Software, Algorithms, and Artificial Intelligence Used in Employment Selection Procedures Under Title VII. Technical assistance document (removed from eeoc.gov early 2025; archived). https://web.archive.org/web/20250102220802/https://www.eeoc.gov/laws/guidance/select-issues-assessing-adverse-impact-software-algorithms-and-artificial link

Federal Trade Commission and Consumer Financial Protection Bureau (2023, October 12). Settlement to Require Trans Union to Pay $15 Million over Charges It Failed to Ensure Accuracy of Tenant Screening Reports (a DIFFERENT product; not about sanctions name screening) https://www.ftc.gov/news-events/news/press-releases/2023/10/ftc-cfpb-settlement-require-trans-union-pay-15-million-over-charges-it-failed-ensure-accuracy-tenant link

Blackwell, Caprara, Rountree, Casey, Vanderford, Battis, Early Outcomes from the Los Angeles County Homelessness Prevention Unit (California Policy Lab, UCLA, 2025) https://capolicylab.org/early-outcomes-from-the-los-angeles-county-homelessness-prevention-unit/ link

AI.GOV.UK Knowledge Hub / DSIT (2025). Asylum Case Summarisation (ACS): Streamlining asylum decisions with AI-powered transcript summarisation (tool record, Home Office, phase Alpha). https://ai.gov.uk/knowledge-hub/tools/asylum-case-summarisation-%28acs%29/ link

Alsherif, S. (Open Rights Group) with Deen, S. (2026, June 2). AI in Asylum Decisions: Transparency and Accountability Failures. https://www.openrightsgroup.org/publications/ai-in-asylum-decisions-transparency-and-accountability-failures/ link

Alsherif, S. (Open Rights Group) with Deen, S. (2026, June 2). AI in Asylum Decisions: Transparency and Accountability Failures. https://dataingovernment.blog.gov.uk/2025/05/08/making-the-algorithmic-transparency-recording-standard-atrs-mandatory-across-government/ link

Deck, A. (2023, April 19). AI translation jeopardizes Afghan asylum claims. Rest of World; with Context/Thomson Reuters Foundation companion. https://restofworld.org/2023/ai-translation-errors-afghan-refugees-asylum/ link

Deck, A. (2023, April 19). AI translation jeopardizes Afghan asylum claims. Rest of World; with Context/Thomson Reuters Foundation companion. https://www.context.news/ai/ais-insane-translation-mistakes-endanger-us-asylum-cases link

Foxglove (2020, August 4). Home Office says it will abandon its 'racist visa algorithm' - after we sued them; with Computer Weekly corroboration. https://www.foxglove.org.uk/2020/08/04/home-office-says-it-will-abandon-its-racist-visa-algorithm-after-we-sued-them/ link

Foxglove (2020, August 4). Home Office says it will abandon its 'racist visa algorithm' - after we sued them; with Computer Weekly corroboration. https://www.computerweekly.com/news/252487195/Home-Office-drops-racist-visa-algorithm link

Heilweil, R. (2023, November 16). Homeland Security updates AI inventory with multiple immigration-related use cases. FedScoop. https://fedscoop.com/dhs-citizenship-immigration-services-ai-use-cases/ link

Home Office / UK Visas and Immigration (2026, May 29). Facial age estimation: Using AI to support initial age decisions (A guide) (accessible). GOV.UK guidance. https://www.gov.uk/government/publications/facial-age-estimation/facial-age-estimation-using-ai-to-support-initial-age-decisions-a-guide-accessible link

Independent Chief Inspector of Borders and Immigration (2025, July 22). An inspection of the Home Office's use of age assessments (July 2024 - February 2025). https://www.gov.uk/government/publications/an-inspection-of-the-home-offices-use-of-age-assessments-july-2024-february-2025 link

Lulamae, J. (2022, September 5). The BAMF's controversial dialect recognition software: new languages and an EU pilot project. AlgorithmWatch; with Beck, J. (2026), Verfassungsblog legal analysis (https://doi.org/10.59704/b22636dc94f60b29). https://verfassungsblog.de/dialect-recognition-software-dias-law/ DOI

National Audit Office (2025, December 10). An analysis of the asylum system. HC 1517, Session 2024-26. https://www.nao.org.uk/reports/an-analysis-of-the-asylum-system/ link

Privacy International (2024, October 17). Automating the hostile environment: uncovering the secretive Home Office algorithm at the heart of immigration enforcement (IPIC); with the 2025 ICO complaint and the primary FOI trail. https://www.whatdotheyknow.com/request/identify_and_prioritise_immigrat_3 link

Scheel, S. (2024). Epistemic domination by data extraction: questioning the use of biometrics and mobile phone data analysis in asylum procedures. Journal of Ethnic and Migration Studies, 50(9), 2289-2308. https://doi.org/10.1080/1369183X.2024.2307782 https://pmc.ncbi.nlm.nih.gov/articles/PMC11034547/ DOI

Privacy International (2024, October 17). Automating the hostile environment: uncovering the secretive Home Office algorithm at the heart of immigration enforcement (IPIC); with the 2025 ICO complaint and the primary FOI trail. https://privacyinternational.org/news-analysis/5452/automating-hostile-environment-uncovering-secretive-home-office-algorithm-heart link

Privacy International (2024, October 17). Automating the hostile environment: uncovering the secretive Home Office algorithm at the heart of immigration enforcement (IPIC); with the 2025 ICO complaint and the primary FOI trail. https://privacyinternational.org/press-release/5640/privacy-international-issues-complaint-uk-regulator-regarding-deployment-two link

Ahangar, M.N., Farhat, Z.A., & Sivanathan, A. (2025). AI Trustworthiness in Manufacturing: Challenges, Toolkits, and the Path to Industry 5.0. Sensors, 25(14), 4357. https://doi.org/10.3390/s25144357 https://pmc.ncbi.nlm.nih.gov/articles/PMC12298069/ DOI

Ahangar, M.N., Farhat, Z.A., Sivanathan, A., Ketheesram, N., & Kaur, S. (2026). Explainable AI-Driven Quality and Condition Monitoring in Smart Manufacturing. Sensors, 26(3), 911. https://doi.org/10.3390/s26030911 https://www.mdpi.com/1424-8220/26/3/911 DOI

Hermansa, M., Kozielski, M., Michalak, M., Szczyrba, K., Wróbel, Ł., & Sikora, M. (2021). Sensor-Based Predictive Maintenance with Reduction of False Alarms — A Case Study in Heavy Industry. Sensors, 22(1), 226. https://doi.org/10.3390/s22010226 https://pmc.ncbi.nlm.nih.gov/articles/PMC8749854/ DOI

Kovalenko, I., Barton, K., Moyne, J., & Tilbury, D.M. (2023). Opportunities and Challenges to Integrate Artificial Intelligence into Manufacturing Systems: Thoughts from a Panel Discussion. https://doi.org/10.48550/arXiv.2303.11139 https://arxiv.org/abs/2303.11139 DOI

Landing AI via PR Newswire (2020, October 21). Landing AI Unveils AI Visual Inspection Platform to Improve Quality and Reduce Costs for Manufacturers Worldwide. https://www.prnewswire.com/news-releases/landing-ai-unveils-ai-visual-inspection-platform-to-improve-quality-and-reduce-costs-for-manufacturers-worldwide-301157094.html link

NIST Manufacturing Extension Partnership (2026, May 13). The Rise of Artificial Intelligence (AI) in U.S. Manufacturing. https://www.nist.gov/mep/rise-artificial-intelligence-ai-us-manufacturing-text-only link

Parasuraman, R., & Manzey, D.H. (2010). Complacency and Bias in Human Use of Automation: An Attentional Integration. Human Factors, 52(3), 381-410. https://doi.org/10.1177/0018720810376055 DOI

Pham, T.M.T., Premkumar, K., Naili, M., & Yang, J. (2025). Time to Retrain? Detecting Concept Drifts in Machine Learning Systems. In ICSE 2025. https://doi.org/10.48550/arXiv.2410.09190 https://arxiv.org/abs/2410.09190 DOI

Sundaram, S., & Zeid, A. (2023). Artificial Intelligence-Based Smart Quality Inspection for Manufacturing. Micromachines, 14(3), 570. https://doi.org/10.3390/mi14030570 https://pmc.ncbi.nlm.nih.gov/articles/PMC10058274/ DOI

Veillon, R., Shabushnig, J., Aabye-Hansen, L., et al. (2023). Applying Machine Learning to the Visual Inspection of Filled Injectable Drug Products. PDA Journal of Pharmaceutical Science and Technology, 77(5), 376-401. https://doi.org/10.5731/pdajpst.2022.012796 https://journal.pda.org/content/77/5/376 DOI

Just Auto (2018, October 17). Audi develops AI software for quality inspections in press shops. https://www.just-auto.com/news/audi-develops-ai-software-for-quality-inspections-in-press-shops/ link

Lean Enterprise Institute. Automatic Line Stop (Lean Lexicon). https://www.lean.org/lexicon-terms/automatic-line-stop/ link

BMW Group PressClub (2025, April 28). Artificial intelligence as a quality booster (GenAI4Q pilot, Plant Regensburg). https://www.press.bmwgroup.com/global/article/detail/T0449729EN/artificial-intelligence-as-a-quality-booster?language=en link

Metrology and Quality News (2026, July 6). BMW Group Advances Use of Physical AI in Production (AIQX, Plant Spartanburg). https://metrology.news/bmw-group-advances-use-of-physical-ai-in-production/ link

Romeo, G., & Conti, D. (2025). Exploring automation bias in human-AI collaboration: a review and implications for explainable AI. AI & Society. https://doi.org/10.1007/s00146-025-02422-7 https://link.springer.com/article/10.1007/s00146-025-02422-7 DOI

Wittbold, K. (2026, June 18). Why Your Team Has Stopped Trusting Their Predictive Maintenance Alerts. Augury blog. https://www.augury.com/blog/machine-health/why-your-team-has-stopped-trusting-their-predictive-maintenance-alerts/ link

Das, J., O'Connor, T.F., Fisher, A.C., et al. (2025). Public feedback to FDA on regulatory considerations for AI in drug manufacturing. AAPS Open, 11, 10. https://doi.org/10.1186/s41120-025-00110-w https://link.springer.com/article/10.1186/s41120-025-00110-w DOI

U.S. FDA, CDER/OPQ (2023). Discussion Paper: Artificial Intelligence in Drug Manufacturing. Docket FDA-2023-N-0487. https://www.fda.gov/media/165743/download link

RCR Wireless News (2016, September 12). Case study: Siemens reduces train failures with Teradata Aster (Renfe Velaro E predictive maintenance). https://www.rcrwireless.com/20160912/big-data-analytics/siemens-train-teradata-tag31-tag99 link

FTC, FTC Finalizes Order with DoNotPay That Prohibits Deceptive AI Lawyer Claims (Feb 2025) https://www.ftc.gov/news-events/news/press-releases/2025/02/ftc-finalizes-order-donotpay-prohibits-deceptive-ai-lawyer-claims-imposes-monetary-relief-requires link

Magesh et al., Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools (Stanford RegLab, arXiv 2405.20362) https://arxiv.org/abs/2405.20362 link

Bartlett, R., Morse, A., Stanton, R., & Wallace, N. (2022). Consumer-Lending Discrimination in the FinTech Era. Journal of Financial Economics, 143(1), 30-56. https://doi.org/10.1016/j.jfineco.2021.05.047 https://www.nber.org/system/files/working_papers/w25943/w25943.pdf DOI

Blattner, L., & Nelson, S. (2021). How Costly is Noise? Data and Disparities in Consumer Credit. Working paper (Stanford GSB / Chicago Booth). https://doi.org/10.48550/arXiv.2105.07554 https://arxiv.org/abs/2105.07554 DOI

Board of Governors of the Federal Reserve System & OCC (2011). SR Letter 11-7: Supervisory Guidance on Model Risk Management (superseded April 17, 2026 by SR 26-2). https://www.federalreserve.gov/supervisionreg/srletters/SR2602.htm link

Consumer Financial Protection Bureau (2022, 2023). Circular 2022-03: Adverse action notification requirements in connection with credit decisions based on complex algorithms; and Circular 2023-03 on Regulation B sample forms. https://www.consumerfinance.gov/compliance/circulars/circular-2023-03-adverse-action-notification-requirements-and-the-proper-use-of-the-cfpbs-sample-forms-provided-in-regulation-b/ link

Consumer Financial Protection Bureau (2023, June 6). Chatbots in Consumer Finance. CFPB Issue Spotlight. https://www.consumerfinance.gov/data-research/research-reports/chatbots-in-consumer-finance/chatbots-in-consumer-finance/ link

FinRegLab (2019). The Use of Cash-Flow Data in Underwriting Credit: Empirical Research Findings; and FinRegLab (2025), Advancing the Credit Ecosystem: Machine Learning & Cash Flow Data in Consumer Underwriting. https://finreglab.org/research/the-use-of-cash-flow-data-in-underwriting-credit-empirical-research-findings/ link

FinRegLab (2019). The Use of Cash-Flow Data in Underwriting Credit: Empirical Research Findings; and FinRegLab (2025), Advancing the Credit Ecosystem: Machine Learning & Cash Flow Data in Consumer Underwriting. https://finreglab.org/research/advancing-the-credit-ecosystem-machine-learning-cash-flow-data-in-consumer-underwriting/ link

Fuster, A., Goldsmith-Pinkham, P., Ramadorai, T., & Walther, A. (2022). Predictably Unequal? The Effects of Machine Learning on Credit Markets. The Journal of Finance, 77(1), 5-47. https://doi.org/10.1111/jofi.13090 https://haas.berkeley.edu/wp-content/uploads/archive/Predictably-Unequal-The-Effects-of-Machine-Learning-on-Credit-Markets.pdf DOI

Jagtiani, J., & Lemieux, C. (2019). The Roles of Alternative Data and Machine Learning in Fintech Lending: Evidence from the LendingClub Consumer Platform. Financial Management, 48(4), 1009-1029. https://doi.org/10.1111/fima.12295 https://www.philadelphiafed.org/-/media/frbp/assets/working-papers/2018/wp18-15r.pdf DOI

Jagtiani, J., Lemieux, C., & Goldstein, B. (2023). Did Fintech Loans Default More During the COVID-19 Pandemic? Were Fintech Firms 'Cream-Skimming' the Best Borrowers? Federal Reserve Bank of Philadelphia Working Paper 23-26. https://www.philadelphiafed.org/-/media/frbp/assets/working-papers/2023/wp23-26.pdf link

Martinez, E., & Kirchner, L. (2021, August 25). The Secret Bias Hidden in Mortgage-Approval Algorithms. The Markup (with Associated Press). https://themarkup.org/denied/2021/08/25/the-secret-bias-hidden-in-mortgage-approval-algorithms link

OCC, Federal Reserve, FDIC, CFPB & NCUA (2021). Request for Information and Comment on Financial Institutions' Use of Artificial Intelligence, Including Machine Learning. 86 FR 16837. https://www.federalregister.gov/documents/2021/03/31/2021-06607/request-for-information-and-comment-on-financial-institutions-use-of-artificial-intelligence link

Office of the Comptroller of the Currency (2021). Model Risk Management, Comptroller's Handbook, Version 1.0 (OCC Bulletin 2021-39). https://www.occ.gov/news-issuances/bulletins/2021/bulletin-2021-39.html link

TrueAccord (c. 2022). Snap Finance: Putting Digital-First, Machine Learning-Driven Collections to the Test (client case study). https://pages.trueaccord.com/snap-finance-case-study.html link

Consumer Financial Protection Bureau (2022). Circular 2022-03: Adverse action notification requirements in connection with credit decisions based on complex algorithms; and (2023) Circular 2023-03: Adverse action notification requirements and the proper use of the CFPB's sample forms provided in Regulation B https://www.consumerfinance.gov/compliance/circulars/circular-2022-03-adverse-action-notification-requirements-in-connection-with-credit-decisions-based-on-complex-algorithms/ link

Board of Governors of the Federal Reserve System & OCC (2011). SR Letter 11-7: Supervisory Guidance on Model Risk Management (superseded April 17, 2026 by SR 26-2). https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm link

Office of the Massachusetts Attorney General (2025, July 10). AG Campbell Announces $2.5 Million Settlement With Student Loan Lender For Unlawful Practices Through AI Use (Assurance of Discontinuance, Earnest Operations LLC). https://www.mass.gov/news/ag-campbell-announces-25-million-settlement-with-student-loan-lender-for-unlawful-practices-through-ai-use-other-consumer-protection-violations link

New York State Department of Financial Services (2021, March 23). Report on Apple Card Investigation. https://www.dfs.ny.gov/reports_and_publications/press_releases/pr202103231 link

Consumer Financial Protection Bureau (2022, August 10). Consent Order, In the Matter of Hello Digit, LLC, File No. 2022-CFPB-0007 https://files.consumerfinance.gov/f/documents/cfpb_hello-digit-llc_consent-order_2022-08.pdf link

Consumer Financial Protection Bureau — Ficklin, P.A., & Watkins, P. (2019). An update on credit access and the Bureau's first No-Action Letter. CFPB Blog. https://www.consumerfinance.gov/about-us/blog/update-credit-access-and-no-action-letter/ link

Relman Colfax PLLC (2021-2024). Fair Lending Monitorship of Upstart Network's Lending Model (Initial, Second, Third, and Final Reports). https://www.relmanlaw.com/cases-upstart-network-fair-lending-counseling link

Harknett, K., Schneider, D., & Irwin, V. (2021). Improving health and economic security by reducing work schedule uncertainty. PNAS, 118(42), e2107828118. https://doi.org/10.1073/pnas.2107828118 DOI

Nguyen, A. (2021). The Constant Boss: Labor Under Digital Surveillance. Data & Society Research Institute. https://datasociety.net/library/the-constant-boss/ link

Office of the City Auditor, City of Seattle, with the West Coast Poverty Center (2019, December). Evaluation of Seattle's Secure Scheduling Ordinance: Year 1 Findings. https://seattle.gov/documents/Departments/CityAuditor/auditreports/SSO_EvaluationYear1Report_122019.pdf link

Schneider, D., & Harknett, K. (2019). Consequences of Routine Work-Schedule Instability for Worker Health and Well-Being. American Sociological Review, 84(1), 82-114. https://doi.org/10.1177/0003122418823184 https://shift.hks.harvard.edu/files/2019/01/Consequences-of-Routine-Work-Schedule-Instability-for-Worker-Health-and-Wellbeing.pdf DOI

Strategic Organizing Center (2022, April). The Injury Machine: How Amazon's Production System Hurts Workers. https://thesoc.org/resources/the-injury-machine-how-amazons-production-system-hurts-workers/ link

U.S. Department of Labor, OSHA (2023, January 18 and February 1). Federal safety inspections at Amazon warehouse facilities find company exposed workers to ergonomic, struck-by hazards (national news releases). https://www.osha.gov/news/newsreleases/osha-national-news-release/20230201 link

U.S. Department of Labor, Occupational Safety and Health Administration (2024, December 19). US Department of Labor announces settlement with Amazon requiring corporate-wide ergonomic measures at facilities nationwide https://www.osha.gov/news/newsreleases/osha-national-news-release/20241219 link

U.S. Senate Committee on Health, Education, Labor, and Pensions (2024, December 15). The Injury-Productivity Trade-off: How Amazon's Obsession with Speed Creates Uniquely Dangerous Warehouses (Majority Staff Report) https://www.help.senate.gov/imo/media/doc/amazon_investigation.pdf link

Allgor, R., Cezik, T., & Chen, D. (2023). Algorithm for Robotic Picking in Amazon Fulfillment Centers Enables Humans and Robots to Work Together Effectively. INFORMS Journal on Applied Analytics, 53(4). https://doi.org/10.1287/inte.2022.1143 DOI

Cheon, E., & Erickson, I. (2025). Fulfillment of the Work Games: Warehouse Workers' Experiences with Algorithmic Management. Proceedings of the ACM on Human-Computer Interaction (CSCW). https://doi.org/10.1145/3757409 https://arxiv.org/abs/2508.09438 DOI

U.S. Department of Labor, OSHA (2023, January 18 and February 1). Federal safety inspections at Amazon warehouse facilities find company exposed workers to ergonomic, struck-by hazards (national news releases). https://www.osha.gov/news/newsreleases/osha-national-news-release/20230118 link

Holland, C., Levis, J., Nuggehalli, R., Santilli, B., & Winters, J. (2017). UPS Optimizes Delivery Routes. Interfaces, 47(1), 8-23. https://doi.org/10.1287/inte.2016.0875 DOI

Levy, K. (2023). Data Driven: Truckers, Technology, and the New Workplace Surveillance. Princeton University Press. https://press.princeton.edu/books/hardcover/9780691175300/data-driven link

Federal Trade Commission, FTC Launches Inquiry into AI Chatbots Acting as Companions (2025) https://www.ftc.gov/news-events/news/press-releases/2025/09/ftc-launches-inquiry-ai-chatbots-acting-companions link

NPR, An eating disorders chatbot offered dieting advice, raising fears about AI in health (2023) https://www.npr.org/sections/health-shots/2023/06/08/1180838096/an-eating-disorders-chatbot-offered-dieting-advice-raising-fears-about-ai-in-hea link

The War Horse, Hiring, Overtime, and AI: VA Is Processing Veterans' Disability Claims Faster Than Ever (2026) https://thewarhorse.org/ai-veterans-affairs-disability-claims/ link

Harris, Finlay, Meerwijk, Evaluating the accuracy of the VHA REACH VET suicide prediction model for legal involved veterans (npj Mental Health Research, 2025;4:53) https://pmc.ncbi.nlm.nih.gov/articles/PMC12535588/ link

Effectiveness of AI-based interventions in workplace mental health: a systematic review, British Medical Bulletin (2025) https://academic.oup.com/bmb/article/157/1/ldag007/8471777 link

European Commission, Interoperable Europe / Public Sector Tech Watch: VioGen 5.0 https://interoperable-europe.ec.europa.eu/collection/public-sector-tech-watch/viogen-50-discovering-spains-risk-assessment-system-gender-based-violence link

Investigation of Bias in the Automated Assessment of School Violence (ARIA) https://pmc.ncbi.nlm.nih.gov/articles/PMC11431206/ link

Fortune / Center for Public Integrity, AI surveillance in schools is reading students' most personal thoughts (2025) https://fortune.com/2025/03/12/ai-surveillance-schools-investigation-gaggle-safety-management-software/ link

Feathers, T. (2023, April 27). False Alarm: How Wisconsin Uses Race and Income to Label Students 'High Risk'. The Markup (with Chalkbeat). https://themarkup.org/machine-learning/2023/04/27/false-alarm-how-wisconsin-uses-race-and-income-to-label-students-high-risk link

Ayer, L., Boudreaux, B., Paige, J.W., et al. (2023). Artificial Intelligence-Based Student Activity Monitoring for Suicide Risk (RR-A2910-1). RAND Corporation. https://www.rand.org/pubs/research_reports/RRA2910-1.html link

Center for Democracy & Technology (2022, August 3). Hidden Harms: The Misleading Promise of Monitoring Students Online. https://cdt.org/insights/report-hidden-harms-the-misleading-promise-of-monitoring-students-online/ link

Consortium for School Networking (2024, April). 2024 State of EdTech District Leadership Report. https://www.cosn.org/tools-and-resources/resource/2024-state-of-edtech-district-leadership-survey/ link

Knowles, J.E. (2015). Of needles and haystacks: Building an accurate statewide dropout early warning system in Wisconsin. Journal of Educational Data Mining, 7(3), 18-67. https://doi.org/10.5281/zenodo.3554725 https://jedm.educationaldatamining.org/index.php/JEDM/article/view/JEDM082 DOI

Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4(7), 100779. https://doi.org/10.1016/j.patter.2023.100779 DOI

National Foundation for Educational Research (2024). ChatGPT in Lesson Preparation: A Teacher Choices Trial. Evaluation report for the Education Endowment Foundation. https://www.nfer.ac.uk/publications/chatgpt-in-lesson-preparation-a-teacher-choices-trial/ link

Ofqual (2020, August 13). Awarding GCSE, AS & A Levels in Summer 2020: Interim Report. https://www.gov.uk/government/publications/awarding-gcse-as-a-levels-in-summer-2020-interim-report link

Ogletree v. Cleveland State University, No. 1:21-cv-00500 (N.D. Ohio, August 22, 2022); via Higher Ed Dive and Future of Privacy Forum analyses. https://fpf.org/blog/federal-court-deems-universitys-use-of-room-scans-within-the-home-unconstitutional/ link

U.S. Department of Education, Office of Educational Technology (2024, October). Empowering Education Leaders: A Toolkit for Safe, Ethical, and Equitable AI Integration. https://files.eric.ed.gov/fulltext/ED661924.pdf link

Wang, R.E., Ribeiro, A.T., Robinson, C.D., Loeb, S., & Demszky, D. (2024). Tutor CoPilot: A Human-AI Approach for Scaling Real-Time Expertise. EdWorkingPaper 24-1054. https://doi.org/10.48550/arXiv.2410.03017 https://edworkingpapers.com/ai24-1054 DOI

Yoder-Himes, D.R., Asif, A., Kinney, K., et al. (2022). Racial, skin tone, and sex disparities in automated proctoring software. Frontiers in Education, 7, 881449. https://doi.org/10.3389/feduc.2022.881449 https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2022.881449/full DOI

Ogletree v. Cleveland State University, No. 1:21-cv-00500 (N.D. Ohio, August 22, 2022); via Higher Ed Dive and Future of Privacy Forum analyses. https://www.highereddive.com/news/test-proctoring-room-scans-violated-college-students-privacy-judge-rules/630340/ link

Allensworth, E.M., & Easton, J.Q. (2007). What Matters for Staying On-Track and Graduating in Chicago Public Schools. University of Chicago Consortium on School Research. https://consortium.uchicago.edu/publications/what-matters-staying-track-and-graduating-chicago-public-schools-focus-students link

Faria, A.-M., Sorensen, N., Heppen, J., Bowdon, J., Taylor, S., Eisner, R., & Foster, S. (2017). Getting students on track for graduation: Impacts of the Early Warning Intervention and Monitoring System after one year (REL 2017-272). U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory Midwest https://eric.ed.gov/?id=ED573814 link

Wisconsin Department of Public Instruction. WISEdash for Districts: Dropout Early Warning System (DEWS) Dashboards (including the October 12, 2023 retirement notice). https://dpi.wi.gov/wisedash/districts/about-data/dews link

AlAhmadi, B., Axon, L., & Martinovic, I. (2022). 99% False Positives: A Qualitative Study of Security Operations Center Analysts' Perspectives on Security Alarms. In Proceedings of the 31st USENIX Security Symposium, 2783-2800. https://www.usenix.org/conference/usenixsecurity22/presentation/alahmadi link

Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2018). Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy. IEEE Transactions on Neural Networks and Learning Systems, 29(8), 3784-3797. https://doi.org/10.1109/TNNLS.2017.2736643 https://dalpozz.github.io/static/pdf/TNNLS_2017.pdf DOI

Edelman, B.G., Bono, J., Peng, S., Rodriguez, R., & Ho, S. (2023-2024). Randomized Controlled Trials for Microsoft Copilot for Security (SSRN 4648700; productivity-findings whitepaper, Jan 2024). https://www.microsoft.com/content/dam/microsoft/final/en-us/microsoft-product-and-services/microsoft-dynamics-365/pdf/Microsoft-Copilot-for-Security-productivity-findings-Whitepaper-Jan2024.pdf link

Edelman, B.G., Bono, J., Peng, S., Rodriguez, R., & Ho, S. (2023-2024). Randomized Controlled Trials for Microsoft Copilot for Security (SSRN 4648700; productivity-findings whitepaper, Jan 2024). https://arxiv.org/html/2511.13860 link

Kokulu, F.B., Soneji, A., Bao, T., Shoshitaishvili, Y., Zhao, Z., Doupé, A., & Ahn, G.-J. (2019). Matched and Mismatched SOCs: A Qualitative Study on Security Operations Center Issues. In ACM CCS 2019, 1955-1970. https://doi.org/10.1145/3319535.3354239 https://adamdoupe.com/publications/matched-and-mismatched-socs-ccs2019.pdf DOI

Lunghi, D., Simitsis, A., Caelen, O., & Bontempi, G. (2023). Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectives. https://doi.org/10.1145/3600046.3600051 https://arxiv.org/abs/2307.01390 DOI

Office of Senator Elizabeth Warren (2022, October). Facilitating Fraud: How Consumers Defrauded on Zelle are Left High and Dry by the Banks that Created It. U.S. Senate oversight report. https://www.warren.senate.gov/imo/media/doc/ZELLE%20REPORT%20OCTOBER%202022.pdf link

SANS Institute (2024). SANS 2024 SOC Survey. https://www.sans.org/press/announcements/2024-sans-soc-survey-reveals-critical-trends-technologies-cyber-defense link

Sundaramurthy, S.C., Bardas, A.G., Case, J., Ou, X., Wesch, M., McHugh, J., & Rajagopalan, S.R. (2015). A Human Capital Model for Mitigating Security Analyst Burnout. In Eleventh Symposium On Usable Privacy and Security (SOUPS 2015). USENIX Association. https://www.usenix.org/conference/soups2015/proceedings/presentation/sundaramurthy link

Tariq, S., Baruwal Chhetri, M., Nepal, S., & Paris, C. (2025). Alert Fatigue in Security Operations Centres: Research Challenges and Opportunities. ACM Computing Surveys, 57(9), Article 224. https://doi.org/10.1145/3723158 https://dl.acm.org/doi/10.1145/3723158 DOI

Tines (2023). Voice of the SOC 2023 (survey of 900 security-operations professionals). https://www.tines.com/reports/voice-of-the-soc-2023/ link

U.S. Senate Committee on Commerce, Science, and Transportation, Majority Staff (2014). A 'Kill Chain' Analysis of the 2013 Target Data Breach. https://www.commerce.senate.gov/wp-content/uploads/media/doc/2014%200325%20Target%20Kill%20Chain%20Analysis.pdf link

Vermeer, M., Kadenko, N., van Eeten, M., Gañán, C., & Parkin, S. (2023). Alert Alchemy: SOC Workflows and Decisions in the Management of NIDS Rules. In ACM CCS 2023. https://doi.org/10.1145/3576915.3616581 https://repository.tudelft.nl/file/File_267d72ad-a929-4856-b913-a0f3202b7998 DOI

Consumer Financial Protection Bureau (2024, May 7). Consent Order, In the Matter of Chime Financial, Inc., File No. 2024-CFPB-0002. https://files.consumerfinance.gov/f/documents/cfpb_chime-financial-inc-consent-order_2024-05.pdf link

Kessler, C. (2021, July 6). A Banking App Has Been Suddenly Closing Accounts, Sometimes Not Returning Customers' Money. ProPublica. https://www.propublica.org/article/chime link

Teradata (2017). Danske Bank Fights Fraud with Deep Learning and AI (case study EB9821). https://assets.teradata.com/resourceCenter/downloads/CaseStudies/CaseStudy_EB9821_Danske_Bank_Saves_Millions_Fighting_Fraud_With_Deep_Learning_and_AI.pdf link

UK Payment Systems Regulator (2023-2025). APP fraud performance data / APP scams performance reports. https://www.psr.org.uk/information-for-consumers/app-fraud-performance-data/ link

Axelsson, S. (2000). The Base-Rate Fallacy and the Difficulty of Intrusion Detection. ACM Transactions on Information and System Security, 3(3), 186-205. https://doi.org/10.1145/357830.357849 https://dl.acm.org/doi/10.1145/357830.357849 DOI

Google Cloud (2023, June 21). Google Cloud Launches AI-Powered Anti Money Laundering Product for Financial Institutions (with HSBC-reported results). https://www.googlecloudpresscorner.com/2023-06-21-Google-Cloud-Launches-AI-Powered-Anti-Money-Laundering-Product-for-Financial-Institutions link

Bakal, G., Dasdan, A., Katz, Y., Kaufman, M., & Levin, G. (2025). Experience with GitHub Copilot for Developer Productivity at Zoominfo [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2501.13282 https://arxiv.org/abs/2501.13282 DOI

Becker, J., Rush, N., Barnes, E., & Rein, D. (2025). Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity. METR. https://doi.org/10.48550/arXiv.2507.09089 https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/ DOI

Chatterjee, S., Liu, C.L., Rowland, G., & Hogarth, T. (2024). The Impact of AI Tool on Engineering at ANZ Bank: An Empirical Study on GitHub Copilot within Corporate Environment [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2402.05636 https://www.theregister.com/2024/02/10/anz_bank_github_copilot/ DOI

Chowdhury, K., Banik, D., Ferdous, K.M., & Shamim, S.I. (2026). From Industry Claims to Empirical Reality: An Empirical Study of Code Review Agents in Pull Requests [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2604.03196 https://arxiv.org/abs/2604.03196 DOI

Cihan, U., Haratian, V., İçöz, A., et al. (2025). Automated Code Review In Practice. In ICSE 2025 (SEIP). https://doi.org/10.48550/arXiv.2412.18531 https://arxiv.org/abs/2412.18531 DOI

Doe v. GitHub, Inc., Microsoft Corp., OpenAI (N.D. Cal., 4:22-cv-06823; filed Nov. 2022). Class action over Copilot training on licensed open-source code and unattributed output. https://www.bakerlaw.com/the-copilot-litigation/ link

Doe v. GitHub, Inc., Microsoft Corp., OpenAI (N.D. Cal., 4:22-cv-06823; filed Nov. 2022). Class action over Copilot training on licensed open-source code and unattributed output. https://caselaw.findlaw.com/court/us-dis-crt-n-d-cal/2200493.html link

GitClear / Harding, W., et al. (2025). AI Copilot Code Quality: 2025 Look Back at 12 Months of Data. GitClear research report. https://www.gitclear.com/ai_assistant_code_quality_2025_research link

Lee, H.-P., Sarkar, A., Tankelevitch, L., et al. (2025). The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers. In CHI 2025. https://doi.org/10.1145/3706598.3713778 https://dl.acm.org/doi/full/10.1145/3706598.3713778 DOI

Pearce, H., Ahmad, B., Tan, B., Dolan-Gavitt, B., & Karri, R. (2022). Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions. In 43rd IEEE Symposium on Security and Privacy (SP 2022). https://doi.org/10.48550/arXiv.2108.09293 https://arxiv.org/abs/2108.09293 DOI

Peng, S., Kalliamvakou, E., Cihon, P., & Demirer, M. (2023). The Impact of AI on Developer Productivity: Evidence from GitHub Copilot [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2302.06590 https://arxiv.org/abs/2302.06590 DOI

Perry, N., Srivastava, M., Kumar, D., & Boneh, D. (2023). Do Users Write More Insecure Code with AI Assistants? In ACM CCS 2023. https://doi.org/10.1145/3576915.3623157 https://dl.acm.org/doi/10.1145/3576915.3623157 DOI

Stack Overflow (2025). 2025 Stack Overflow Developer Survey (AI section). https://survey.stackoverflow.co/2025/ai link

Vaithilingam, P., Zhang, T., & Glassman, E.L. (2022). Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models. In CHI 2022 Extended Abstracts. https://doi.org/10.1145/3491101.3519665 https://dl.acm.org/doi/10.1145/3491101.3519665 DOI

Google Cloud DORA (2025). State of AI-assisted Software Development (2025 DORA Report). https://dora.dev/dora-report-2025/ link

Tabachnyk, M., & Nikolov, S. (2022). ML-Enhanced Code Completion Improves Developer Productivity. Google Research Blog. https://research.google/blog/ml-enhanced-code-completion-improves-developer-productivity/ link

Cui, Z.K., Demirer, M., Jaffe, S., Musolff, L., Peng, S., & Salz, T. (2025). The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers. Management Science. https://doi.org/10.1287/mnsc.2025.00535 https://pubsonline.informs.org/doi/10.1287/mnsc.2025.00535 DOI

Google Cloud DORA (2024). Accelerate State of DevOps Report 2024. https://dora.dev/research/2024/dora-report/ link

Ziegler, A., Kalliamvakou, E., Li, X.A., et al. (2024). Measuring GitHub Copilot's Impact on Productivity. Communications of the ACM, 67(3). https://doi.org/10.1145/3633453 https://dl.acm.org/doi/10.1145/3633453 DOI

STAT News, Digital therapeutics pioneer Pear's treatments get a second life, a year after bankruptcy (2024) https://www.statnews.com/2024/08/22/pear-pursuecare-reset-digital-therapeutic-substance-abuse/ link

Bank for International Settlements, Governance of AI adoption in central banks (BIS Papers othp90) https://www.bis.org/publ/othp90.pdf link

Stanford HAI, IRS Disproportionately Audits Black Taxpayers (2023), reporting Elzayn et al. https://hai.stanford.edu/news/irs-disproportionately-audits-black-taxpayers link

California State Auditor, Report 2020-628.2, EDD Fraud Prevention During the Pandemic https://information.auditor.ca.gov/reports/2020-628.2/summary.html link

NPR (2023, November 28). Sports Illustrated is accused of posting articles by writers created by AI https://www.npr.org/2023/11/28/1215693615/sports-illustrated-is-accused-of-posting-articles-by-writers-created-by-ai link

'The Illusion of Progress' (arXiv:2508.08285) — LLM-as-Judge Precision 0.736 / Recall 0.957 / F1 0.832 vs human consensus on QA. https://arxiv.org/abs/2508.08285 link

Datadog LLM-as-a-judge (2025) — detection F1 drops substantially from HaluBench to the harder RAGTruth; harder hallucinations are harder to catch.

Faithful RAG leaderboard (arXiv:2505.04847) — FaithJudge with o3-mini-high reaches ~84% balanced accuracy / ~82% F1 on FaithBench (optimistic ceiling). https://arxiv.org/abs/2505.04847 link

Mental-health chatbot detection (arXiv:2604.06216) — GPT judges 54.6% accuracy, 9.3% recall (miss 90.7% of hallucinations); traditional methods F1<0.30 on subjective content. https://arxiv.org/abs/2604.06216 link

Same detection-accuracy literature as catch_at_generation (FaithBench arXiv:2410.13210; arXiv:2508.08285); audit-time detection is bounded by the same hallucination-detection ceiling.

Same detection-accuracy literature as catch_at_generation (FaithBench arXiv:2410.13210; arXiv:2508.08285); audit-time detection is bounded by the same hallucination-detection ceiling.

HalluDetect legal-domain (arXiv:2509.11619) — best mitigation architecture reaches ~96% token accuracy in a FAVORABLE, retrieval-grounded legal setting (optimistic end). https://arxiv.org/abs/2509.11619 link

RAG evaluation survey (arXiv:2405.07437) — factuality evaluation is bounded by knowledge-base coverage and retrieval accuracy; what is checkable depends on what is documented. https://arxiv.org/abs/2405.07437 link

Faithful RAG with Sparse Autoencoders (arXiv:2512.08892) — even with relevant passages retrieved, models contradict evidence / invent details; faithfulness is not guaranteed. https://arxiv.org/abs/2512.08892 link

FaithfulRAG (arXiv:2506.08938) — RAG systems struggle in knowledge-conflict scenarios even when relevant passages are retrieved (pessimistic end). https://arxiv.org/abs/2506.08938 link

Retrieval-augmented COVID-19 fact-checking (PMC12079058) — CRAG/Self-RAG reach 0.972-0.978 accuracy against a curated 130k peer-reviewed corpus (optimistic ceiling). https://pmc.ncbi.nlm.nih.gov/articles/PMC12079058/ link

HALoGEN (arXiv:2501.08292) — best models hallucinate 4%-86% of generated facts depending on domain. https://arxiv.org/abs/2501.08292 link

Karpowicz (2025) — three independent mathematical frameworks (auction theory, proper scoring, log-sum-exp) all conclude no LLM inference mechanism can be simultaneously truthful, etc.

llm-stats.com failure-focused eval (2026) — FactsGrounding 89.1% accuracy => ~10.9% failure on a relatively easy grounded benchmark.

OpenAI (2025), 'Why Language Models Hallucinate' — next-token training plus IDK-penalizing benchmarks push models to bluff; explains the persistent nonzero floor.

Suprmind benchmark digest (2026) — production ChatGPT ~4.8% major-incorrect with reasoning vs ~11.6% without; HealthBench 3.6%->1.6% with GPT-5 thinking.

Xu et al. (2024), 'Hallucination is Inevitable: An Innate Limitation of LLMs' — formal proof that hallucination cannot be eliminated.

Guidi, G., Dominici, F., Gilmour, J., et al., Environmental Burden of United States Data Centers in the Artificial Intelligence Era (2024) https://arxiv.org/abs/2411.09786 link

Moffatt v. Air Canada, 2024 BCCRT 149 (British Columbia Civil Resolution Tribunal, February 14, 2024). https://www.canlii.org/en/bc/bccrt/doc/2024/2024bccrt149/2024bccrt149.html link

Sookman, B.B. (2024, February 19). Moffatt v. Air Canada: A Misrepresentation by an AI Chatbot. McCarthy Tétrault TechLex blog https://www.mccarthy.ca/en/insights/blogs/techlex/moffatt-v-air-canada-misrepresentation-ai-chatbot link

Acteurs Publics, Derriere l'echec mediatique d'Albert, un projet d'IA plus global qui s'ancre dans l'Etat (2026) https://acteurspublics.fr/articles/de-chatbot-experimental-a-socle-interministeriel-pour-lia-de-letat-le-parcours-dalbert-ia/ link

DINUM (numerique.gouv.fr), La DINUM a recu le prix innovation des Victoires des Acteurs publics pour le lancement d'Albert (2024) https://www.numerique.gouv.fr/actualites/la-dinum-a-recu-le-prix-innovation-des-victoires-des-acteurs-publics-pour-le-lancement-d-albert/ link

France services / ANCT, Experimentation d'un modele d'assistance aux conseillers France services base sur l'intelligence artificielle (2024) https://www.france-services.gouv.fr/actualites/experimentation-dun-modele-dassistance-france-services-IA link

Journal du Geek, Gabriel Attal annonce la naissance d'une IA francaise pour revolutionner le service public (2024) https://www.journaldugeek.com/2024/04/24/gabriel-attal-annonce-la-naissance-dune-ia-francaise-pour-revolutionner-le-service-public/ link

Next (next.ink), Albert: l'IA souveraine de la Dinum ne sera pas generalisee dans sa forme actuelle (2026) https://next.ink/brief-article/albert-lia-souveraine-de-la-dinum-ne-sera-pas-generalisee-dans-sa-forme-actuelle/ link

Public Senat, IA, simplification des formulaires, France Services: Gabriel Attal annonce sa feuille de route pour debureaucratiser les demarches administratives (2024) https://www.publicsenat.fr/actualites/politique/ia-simplification-des-formulaires-france-services-gabriel-attal-annonce-sa-feuille-de-route-pour-debureaucratiser-les-demarches-administratives link

Solidaires Finances Publiques, Entre ici Albert, au pantheon des IA souveraines (2026) https://solidairesfinancespubliques.org/le-syndicat/dossiers/ia-a-la-dgfip/7192-albert-france-service.html link

Weka.fr (AFP dispatch), Albert, l'outil d'IA generative, experimente a France Services ne sera pas generalise (2026) https://www.weka.fr/actualite/administration/article/albert-l-outil-d-ia-generative-experimente-a-france-services-ne-sera-pas-generalise-209194/ link

Goldhaber-Fiebert & Prince (Stanford), Impact evaluation summary: Allegheny Family Screening Tool (Allegheny County DHS, April 2019) https://analytics.alleghenycounty.us/wp-content/uploads/2019/05/Impact-Evaluation-Summary-from-16-ACDHS-26_PredictiveRisk_Package_050119_FINAL-5.pdf link

American Civil Liberties Union, How Policy Hidden in an Algorithm Is Threatening Families in This Pennsylvania County (ACLU, 2023) https://www.aclu.org/news/womens-rights/how-policy-hidden-in-an-algorithm-is-threatening-families-in-this-pennsylvania-county link

Associated Press (Ho and Burke), Child Welfare Algorithm Used by Allegheny County DHS Faces Justice Department Scrutiny (90.5 WESA, 2023) https://www.wesanews.org/politics-government/2023-01-31/child-welfare-algorithm-used-by-allegheny-county-dhs-faces-justice-department-scrutiny link

Centre for Social Data Analytics (AUT), AFST evaluation summary https://csda.aut.ac.nz/news-and-events/2019/allegheny-family-screening-tool-evaluation-improved-decision-accuracy,-reduced-disparities link

Eubanks, Automating Inequality (2018); AP investigation (Ho & Burke, 2022) https://www.pbs.org/newshour/nation/ap-report-doj-examining-ai-screening-tool-used-by-pa-child-welfare-agency link

Gerchick et al., The Devil Is in the Details: Interrogating Values Embedded in the Allegheny Family Screening Tool (ACLU and Human Rights Data Analysis Group, ACM FAccT 2023) https://www.aclu.org/the-devil-is-in-the-details-interrogating-values-embedded-in-the-allegheny-family-screening-tool link

Goldhaber-Fiebert and Prince, Impact Evaluation of the Allegheny Family Screening Tool Phase 2 Summary (Stanford University for Allegheny County DHS, 2023) https://analytics.alleghenycounty.us/wp-content/uploads/2024/05/23-ACDHS-19_FamilyScreeningToolUpdate_Summary.pdf link

Ho and Burke, Opaque AI Tool May Flag Parents With Disabilities (Associated Press via 90.5 WESA, 2023) https://www.wesanews.org/health-science-tech/2023-03-18/ai-parents-disabilities-family link

Rittenhouse, Algorithms, Humans and Racial Disparities in Child Protective Services https://krittenh.github.io/katherine-rittenhouse.com/Rittenhouse_Algorithms.pdf link

Stapleton, Cheng, Kawakami et al., Extended Analysis of How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions (arXiv 2204.13872) https://arxiv.org/abs/2204.13872 link

Stapleton, How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions (CW360, Center for Advanced Studies in Child Welfare, University of Minnesota, 2025) https://cascw.umn.edu/cw360deg-spring-2025/how-child-welfare-workers-reduce-racial-disparities-algorithmic-decisions link

Ho and Burke, How an Algorithm That Screens for Child Neglect Could Harden Racial Disparities (Associated Press via PBS NewsHour, 2022) https://www.pbs.org/newshour/nation/how-an-algorithm-that-screens-for-child-neglect-could-harden-racial-disparities link

Zhang, L., & Denby-Brinson, R. (2026). AI in Child Welfare and Family Services. In R. An & M. A. Lindsey (Eds.), Artificial Intelligence in Social Work: Bridging Technology and Humanity. Springer. Open access, CC BY 4.0 https://doi.org/10.1007/978-3-032-18443-6_4 DOI

Vaithianathan, Benavides-Prado, Rebbe & Putnam-Hornstein, Using a Predictive Risk Model to Prioritize Families for Prevention Services: The Hello Baby Program in Allegheny County, PA, Prevention Science (2025) https://pmc.ncbi.nlm.nih.gov/articles/PMC12064473/ link

Allegheny County Department of Human Services, Children, Youth and Families: Response to Independent Reviews of the Hello Baby Predictive Risk Model (2020) https://analytics.alleghenycounty.us/wp-content/uploads/2020/09/20-ACDHS-18-HelloBaby-CYF-Response_v2.pdf link

Centre for Social Data Analytics (AUT) for Allegheny County DHS, Implementing the Hello Baby Prevention Program in Allegheny County: Methodology Report Version I (2020) https://analytics.alleghenycounty.us/wp-content/uploads/2020/12/Hello-Baby-Methodology-v6.pdf link

Lery, Wulczyn, Benatar, Zhou, Huhr, Norwitt & Brooks (Urban Institute / Chapin Hall), Evaluation Findings from Hello Baby in Allegheny County, Pennsylvania (2025) https://www.urban.org/sites/default/files/additional-materials/Evaluation_Findings_from_Hello_Baby_in_Allegheny_County_Pennsylvania.pdf link

National Coalition for Child Protection Reform, Cutting Through the Spin About Predictive Analytics in Child Welfare (Hello Baby Ethics) (2022) https://www.nccprblog.org/2022/02/hellobabyethics.html link

Samant, Horowitz, Xu & Beiers (ACLU), Family Surveillance by Algorithm: The Rapidly Spreading Tools Few Have Heard Of (2021) https://www.aclu.org/wp-content/uploads/document/2021.09.28_Family_Surveillance_by_Algorithm.pdf link

Allegheny County Department of Human Services (Allegheny Analytics), Allegheny Housing Assessment (AHA) Frequently Asked Questions (January 2026) https://analytics.alleghenycounty.us/wp-content/uploads/2026/01/AHA-FAQs-Update_Jan_2026.pdf link

Allegheny County Department of Human Services (Allegheny Analytics), Improving Prioritization of Housing Services: Implementation of the Allegheny Housing Assessment (AHA) and the Mental Health Allegheny Housing Assessment (MH-AHA) (January 2026) https://analytics.alleghenycounty.us/2026/01/16/improving-prioritization-of-housing-services-implementation-of-the-allegheny-housing-assessment/ link

American Civil Liberties Union (Samant, Horowitz, Beiers, Xu), Family Surveillance by Algorithm: The Rapidly Spreading Tools Few Have Heard Of (2021) https://www.aclu.org/news/womens-rights/family-surveillance-by-algorithm-the-rapidly-spreading-tools-few-have-heard-of link

Cheng, Drayton, Chouldechova and Vaithianathan, Algorithm-Assisted Decision Making and Racial Disparities in Housing: A Study of the Allegheny Housing Assessment Tool (Proceedings of the 2024 AAAI/ACM Conference on AI, Ethics, and Society; arXiv:2407.21209) https://arxiv.org/abs/2407.21209 link

Eticas Research and Consulting (team led by Carlos Castillo), Algorithmic Impact Assessment of the predictive system for risk of homelessness developed for the Allegheny County (2020) https://analytics.alleghenycounty.us/wp-content/uploads/2020/08/Eticas-assessment.pdf link

Eubanks, A Response to Allegheny County DHS (companion blog post to Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor, St. Martin's Press, 2018) https://virginia-eubanks.com/2018/02/16/a-response-to-allegheny-county-dhs/ link

Vaithianathan and Kithulgoda, Using Predictive Risk Modeling to Prioritize Services for People Experiencing Homelessness in Allegheny County: Methodology Paper for the Allegheny Housing Assessment (Centre for Social Data Analytics, Auckland University of Technology, 2020) https://www.alleghenycountyanalytics.us/wp-content/uploads/2021/01/20-ACDHS-24-MethodologyReport_01142021_v2.pdf link

Central Bank of Kenya (2020, April 8). The Banking (Credit Reference Bureau) Regulations, 2020 (Legal Notice No. 55, Kenya Gazette Supplement No. 42) https://www.centralbank.go.ke/wp-content/uploads/2021/05/Credit-Reference-Bureau-Regulations-2020.pdf link

Central Bank of Kenya (2022, March 18). The Central Bank of Kenya (Digital Credit Providers) Regulations, 2022 (Legal Notice No. 46, Kenya Gazette Supplement No. 45) https://www.centralbank.go.ke/wp-content/uploads/2022/03/L-.N.-No.-46-Central-Bank-of-Kenya-Digital-Credit-Providers-Regulations-2022.pdf link

Central Bank of Kenya (2025, September 4). Press Release: Licensing of Digital Credit Providers https://www.centralbank.go.ke/uploads/press_releases/1415166150_Press%20Release%20on%20licensing%20of%20Digital%20Credit%20Providers%20(DCP's)%20-September%202025.pdf link

Central Bank of Kenya (2025, August 7). Draft Non-Deposit Taking Credit Providers (NDTCPs) Regulations, 2025 https://www.centralbank.go.ke/2025/08/07/draft-non-deposit-taking-credit-providers-ndtcps-regulations-2025/ link

Central Bank of Kenya (2026, April 14). Press Release: Licensing of Digital Credit Providers https://www.centralbank.go.ke/uploads/press_releases/263342722_Press%20Release%20-%20Licensing%20of%20%20Digital%20Credit%20Providers.pdf link

Competition Authority of Kenya and Innovations for Poverty Action (Putman, D., Mazer, R., & Blackmon, W.) (2021, May). Report on the Competition Authority of Kenya Digital Credit Market Inquiry https://poverty-action.org/sites/default/files/2023-05/Digital_Credit_Market_Inquiry_Report_2021.pdf link

Cook, T., & McKay, C. (2015, April). How M-Shwari Works: The Story So Far (Forum No. 10). CGAP and FSD Kenya https://www.cgap.org/sites/default/files/Forum-How-M-Shwari-Works-Apr-2015.pdf link

Cytonn Investments (2020, August 12). Safaricom Raises M-Shwari's Minimum Loan Limit to Ksh 2000 (news digest) https://cytonnreport.com/news/safaricom-raises-m-shwari-s-minimum-loan-limit-to-ksh-2000 link

Johnen, C., Parlasca, M., & Musshoff, O. (2021). Promises and pitfalls of digital credit: Empirical evidence from Kenya. PLOS ONE, 16(7), e0255215 https://doi.org/10.1371/journal.pone.0255215 DOI

Office of the Data Protection Commissioner, Kenya (2023, December). Guidance Note for Digital Credit Providers https://www.odpc.go.ke/wp-content/uploads/2024/02/ODPC-Guidance-Note-for-Digital-Credit-Providers.pdf link

Suri, T., Bharadwaj, P., & Jack, W. (2021). Fintech and household resilience to shocks: Evidence from digital loans in Kenya. Journal of Development Economics, 153, 102697 (read in the NBER Working Paper 25604 version, February 2019) https://doi.org/10.1016/j.jdeveco.2021.102697 DOI

U.S. Department of Labor, Occupational Safety and Health Administration (2024, December 19). US Department of Labor announces settlement with Amazon requiring corporate-wide ergonomic measures at facilities nationwide https://www.osha.gov/news/newsreleases/osha-national-news-release/20241219 link

U.S. Senate Committee on Health, Education, Labor, and Pensions (2024, December 15). The Injury-Productivity Trade-off: How Amazon's Obsession with Speed Creates Uniquely Dangerous Warehouses (Majority Staff Report) https://www.help.senate.gov/imo/media/doc/amazon_investigation.pdf link

ABC7 Los Angeles (2024). Amazon fined $5.9 million for allegedly violating California's warehouse quota law at its Moreno Valley and Redlands warehouses https://abc7.com/post/amazon-fined-59-million-allegedly-violating-californias-warehouse/14972284/ link

Assembly Bill 701 (2021), Warehouse distribution centers; California Labor Code sections 2100-2112 https://leginfo.legislature.ca.gov/faces/billTextClient.xhtml?bill_id=202120220AB701 link

Business & Human Rights Resource Centre (2019). Amazon letter shows company uses an automated system to monitor and terminate employees https://www.business-humanrights.org/en/latest-news/amazon-letter-shows-company-uses-an-automated-system-to-monitor-terminate-employees/ link

California Department of Industrial Relations (2024). Labor Commissioner's Office Cites Amazon $5.9 Million for Warehouse Quotas Violations (news release 2024-46) https://www.dir.ca.gov/DIRNews/2024/2024-46.html link

CBS News (2019). Amazon under fire for software that recommends firing workers https://www.cbsnews.com/news/amazon-under-fire-for-software-that-recommends-firing-workers/ link

CBS News (2025). Amazon sued in class action lawsuit over workplace absence practices (Lyster v. Amazon.com Services LLC, S.D.N.Y., filed 12 November 2025) https://www.cbsnews.com/news/amazon-class-action-lawsuit-warehouse-workplace-absence-practices/ link

Center for Urban Economic Development, University of Illinois Chicago (2023). Pain Points: Data on Work Intensity, Monitoring, and Health at Amazon Warehouses (National Survey of Amazon Warehouse Workers) https://cued.uic.edu/wp-content/uploads/sites/219/2023/10/Pain-Points_Final_Oct2023.pdf link

Lecher, C. (2019, April 25). How Amazon automatically tracks and fires warehouse workers for 'productivity'. The Verge; host blocked from the verifying environment on 2026-08-28 and not read, with the operative sentence and every figure corroborated by the three records below https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations link

Littler Mendelson (2026). Warehouse Quota Notice Laws: Connecticut Joins the Club https://www.littler.com/news-analysis/asap/warehouse-quota-notice-laws-connecticut-joins-club link

Minnesota Department of Labor and Industry (2024). Minnesota OSHA issues citations to Amazon for warehouse distribution worker safety and ergonomic hazards https://dli.mn.gov/node/7026 link

MIT Technology Review (2019). Amazon's system for tracking its warehouse workers can automatically fire them https://www.technologyreview.com/2019/04/26/1021/amazons-system-for-tracking-its-warehouse-workers-can-automatically-fire-them/ link

New York State Department of Labor (2023). Quota Requirements, Prohibitions, and Employee Requests Under the Warehouse Worker Protection Act https://dol.ny.gov/quota-requirements-prohibitions-and-employee-requests-under-warehouse-worker-protection-act link

Wiessner, D. (2023). Amazon beats claim that warehouse quotas are biased against older workers. Reuters, read via syndication because the original was not fetchable https://finance.yahoo.com/news/amazon-beats-claim-warehouse-quotas-193201100.html link

AI Incident Database, Responsible AI Collaborative (2021). Incident 111: Amazon Flex Drivers Allegedly Fired via Automated Employee Evaluations https://incidentdatabase.ai/cite/111/ link

Bajgiran, T. (2022, January 5). Fired by an Algorithm: Amazon Flex and the Case for Human-in-Command Termination Review. OnLabor (Harvard Law School) https://onlabor.org/fired-by-an-algorithm-amazon-flex-and-the-case-for-human-in-command-termination-review/ link

City of Seattle, Office of Labor Standards. App-Based Worker Deactivation Rights Ordinance, Seattle Municipal Code 8.40 (Ordinance 126878, effective 1 January 2025; administrative rules SHRR Chapter 260 effective 24 June 2025), with the City's Notice of Rights https://www.seattle.gov/laborstandards/ordinances/app-based-worker-ordinances/deactivation-rights link

Dent, S. (2021, June 29). Amazon is reportedly using algorithms to fire Flex delivery drivers. Engadget https://www.engadget.com/amazon-algorithms-fire-flex-delivery-drivers-055959081.html link

Federal Trade Commission (2021, February 2). Amazon To Pay $61.7 Million to Settle FTC Charges It Withheld Some Customer Tips from Amazon Flex Drivers; and (2021, November 2). FTC Returns Nearly $60 Million to Drivers Whose Tips Were Illegally Withheld by Amazon https://www.ftc.gov/news-events/news/press-releases/2021/02/amazon-pay-617-million-settle-ftc-charges-it-withheld-some-customer-tips-amazon-flex-drivers link

Human Rights Watch (2025, May 12). The Gig Trap: Algorithmic, Wage and Labor Exploitation in Platform Work in the US https://www.hrw.org/report/2025/05/12/gig-trap/algorithmic-wage-and-labor-exploitation-platform-work-us link

KOMO News (2025, December). Seattle secures $3.7M settlement for Amazon Flex drivers over gig-worker protection rules https://komonews.com/news/local/seattle-amazon-flex-reach-nearly-38m-settlement-over-gig-worker-pay-sick-leave-rules-premium-pay-ordinance-app-based-worker-law link

Office of the Attorney General for the District of Columbia (2022, December 7 and 2025, February 7). Suit and $3.95 million settlement over tips intended for Amazon Flex delivery workers https://oag.dc.gov/release/ag-schwalb-secures-395-million-amazon-resolve link

PBS Wisconsin / Associated Press (2024, March 26). Wisconsin Supreme Court lets ruling stand that declared Amazon Flex delivery drivers to be employees; and New Jersey Department of Labor and Workforce Development (2025, October 20). NJDOL Sues Amazon for Misclassifying Flex Delivery Drivers as Independent Contractors https://www.nj.gov/labor/lwdhome/press/2025/20251021_Amazon.shtml link

Rittmann v. Amazon.com, Inc., No. 19-35381 (9th Cir. 19 August 2020) (M. Smith, J.; Bress, J., dissenting) https://cdn.ca9.uscourts.gov/datastore/opinions/2020/08/19/19-35381.pdf link

Seattle Office of Labor Standards (2025). Resolved Investigations, October to December 2025 https://seattle.gov/laborstandards/investigations/resolved-investigations/october-%E2%80%93-december-2025 link

Soper, S. (2021, June 28). Fired by Bot at Amazon: 'It's You Against the Machine'. Bloomberg; paywalled and blocked from the verifying environment on 2026-08-28 and read in full through the syndication below https://www.bloomberg.com/news/features/2021-06-28/fired-by-bot-amazon-turns-to-machine-managers-and-workers-are-losing-out link

The Spokesman-Review (2021, June 28). Fired by bot at Amazon: 'It's you against the machine' (Bloomberg syndication, full text) https://www.spokesman.com/stories/2021/jun/28/fired-by-bot-at-amazon-its-you-against-the-machine/ link

Uber Technologies, Inc. v. City of Seattle, Nos. 25-228 and 25-231 (9th Cir. 4 March 2026) (Clifton, J.; Bennett, J., dissenting in part) https://cdn.ca9.uscourts.gov/datastore/opinions/2026/03/04/25-231.pdf link

U.S. Senate Committee on Health, Education, Labor, and Pensions (2024, December 15). The Injury-Productivity Trade-off: How Amazon's Obsession with Speed Creates Uniquely Dangerous Warehouses (Majority Staff Report) https://www.help.senate.gov/imo/media/doc/amazon_investigation.pdf link

Allgor, R., Cezik, T., & Chen, D. (2023). Algorithm for Robotic Picking in Amazon Fulfillment Centers Enables Humans and Robots to Work Together Effectively. INFORMS Journal on Applied Analytics, 53(4). https://doi.org/10.1287/inte.2022.1143 DOI

Cheon, E., & Erickson, I. (2025). Fulfillment of the Work Games: Warehouse Workers' Experiences with Algorithmic Management. Proceedings of the ACM on Human-Computer Interaction (CSCW). https://doi.org/10.1145/3757409 https://arxiv.org/abs/2508.09438 DOI

U.S. Department of Labor, OSHA (2023, January 18 and February 1). Federal safety inspections at Amazon warehouse facilities find company exposed workers to ergonomic, struck-by hazards (national news releases). https://www.osha.gov/news/newsreleases/osha-national-news-release/20230118 link

Guo, P., & Hong, P. Y. P. (2026). AI in the Evolving Workplace. In R. An & M. A. Lindsey (Eds.), Artificial Intelligence in Social Work: Bridging Technology and Humanity. Springer. Open access, CC BY 4.0 https://doi.org/10.1007/978-3-032-18443-6_18 DOI

Dastin, J. (2018, October 10). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https://www.euronews.com/business/2018/10/10/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women link

Li, D., Raymond, L.R., & Bergman, P. (2020). Hiring as Exploration. NBER Working Paper 27736 https://www.nber.org/papers/w27736 link

Braun, Geiger, Amsterdam Fair Welfare AI (Inside Amsterdam's high-stakes experiment to create fair welfare AI) (MIT Technology Review, with Lighthouse Reports and Trouw, 2025) https://www.technologyreview.com/2025/06/11/1118233/amsterdam-fair-welfare-ai-discriminatory-algorithms-failure/ link

Lighthouse Reports, Amsterdam's 'Smart Check' welfare-fraud model: fairness methodology (with Trouw and MIT Technology Review, supported by the Pulitzer Center) https://www.lighthousereports.com/methodology/amsterdam-fairness/ link

Algoritmeregister (Dutch national algorithm register), Onderzoekswaardigheid: Slimme check levensonderhoud (Gemeente Amsterdam) (2023, last modified 2025) https://algoritmes.overheid.nl/nl/algoritme/gm0363/95794697/onderzoekswaardigheid-slimme-check-levensonderhoud link

Gemeente.nu, Amsterdam gestopt met AI-pilot voor bijstandsaanvragen (2025) https://www.gemeente.nu/bedrijfsvoering/digitalisering/amsterdam-stopte-met-ai-pilot-voor-bijstandsaanvragen/ link

Lighthouse Reports, amsterdam_fairness (Smart Check bias and model analysis repository) (2025) https://github.com/Lighthouse-Reports/amsterdam_fairness link

Lighthouse Reports, The Limits of Ethical AI (investigation) (2025) https://www.lighthousereports.com/investigation/the-limits-of-ethical-ai/ link

openresearch.amsterdam (City of Amsterdam), Slimme Check: working with a citizen panel for innovation in the social domain (2024) https://openresearch.amsterdam/en/page/109792/slimme-check-working-with-a-citizen-panel-for-innovation-in-the link

Racism and Technology Center, Racist Technology in Action: how the municipality of Amsterdam tried to roll out a fair fraud detection algorithm (2025) https://racismandtechnology.center/2025/07/02/racist-technology-in-action-how-the-municipality-of-amsterdam-tried-to-roll-out-a-fair-fraud-detection-algorithm-spoiler-alert-it-was-a-disaster/ link

Anticipation Hub (IFRC, German Red Cross and Red Cross Red Crescent Climate Centre), global knowledge hub for anticipatory action https://www.anticipation-hub.org/ link

German Red Cross and partners, "Forecast-based Financing" (programme site). https://www.forecast-based-financing.org/ link

United Nations Office for the Coordination of Humanitarian Affairs, "Anticipatory action" (programme framework page). https://www.unocha.org/anticipatory-action link

Food and Agriculture Organization of the United Nations, Bangladesh: Impact of Anticipatory Action - Striking before the floods to protect agricultural livelihoods (FAO, Dhaka, 2021) https://www.fao.org/3/cb4113en/cb4113en.pdf link

Harrigan, Zsoter, Alfieri, Prudhomme, Salamon, Wetterhall, Barnard, Cloke, Pappenberger, GloFAS-ERA5 operational global river discharge reanalysis 1979-present (Earth System Science Data, 12:2043-2060, 2020) https://doi.org/10.5194/essd-12-2043-2020 DOI

United Nations Office for the Coordination of Humanitarian Affairs, Anticipatory Action Framework: Bangladesh Monsoon Floods (endorsed by the Resident Coordinator 30 May 2021; pre-approved by the Emergency Relief Coordinator 3 June 2021) https://www.unocha.org/publications/report/bangladesh/anticipatory-action-framework-bangladesh-monsoon-floods link

United Nations Office for the Coordination of Humanitarian Affairs, Anticipatory Action Framework: Bangladesh Monsoon Floods, 2025 Version (13 May 2025) https://www.unocha.org/publications/report/bangladesh/anticipatory-action-framework-bangladesh-monsoon-floods-2025-version link

Chatterjee, S., Liu, C.L., Rowland, G., & Hogarth, T. (2024). The Impact of AI Tool on Engineering at ANZ Bank: An Empirical Study on GitHub Copilot within Corporate Environment [Preprint] https://arxiv.org/abs/2402.05636 link

Pearce, H., Ahmad, B., Tan, B., Dolan-Gavitt, B., & Karri, R. (2022). Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions. IEEE S&P 2022 https://arxiv.org/abs/2108.09293 link

The Register (2024, February 10). ANZ Bank test drives GitHub Copilot, decides it's worth the effort https://www.theregister.com/2024/02/10/anz_bank_github_copilot/ link

American Civil Liberties Union (2024, May 30). ACLU Files FTC Complaint Against Major Hiring Technology Vendor for Deceptively Marketing Online Hiring Tests as 'Bias Free' https://www.aclu.org/press-releases/aclu-files-ftc-complaint-against-major-hiring-technology-vendor-for-deceptively-marketing-online-hiring-tests-as-bias-free link

American Civil Liberties Union Foundation (2024, May 30). In the Matter of Aon Consulting, Inc.: Complaint and Request for Investigation, Injunction, and Other Relief, filed with the Federal Trade Commission under Section 5 of the FTC Act https://www.aclu.org/wp-content/uploads/2024/05/In-re-Aon-Consulting_FTC-Act-complaint_052924.pdf link

Aon plc (2026). Pre-Hire Talent Assessment (live product marketing page, fetched 28 August 2026) https://www.aon.com/en/capabilities/talent-and-rewards/pre-hire-talent-assessment link

Bloomberg Law, Daily Labor Report (2025, August 22). AI Hiring Tools Elevate Bias Danger for Autistic Job Applicants https://news.bloomberglaw.com/daily-labor-report/ai-hiring-tools-elevate-bias-danger-for-autistic-job-applicants link

Bloomberg Law, Daily Labor Report (2025, March 26). Trump's Disparate Impact Blow Makes AI Bias Claims Even Tougher https://news.bloomberglaw.com/daily-labor-report/trumps-disparate-impact-blow-makes-ai-bias-claims-even-tougher link

Fisher Phillips (2024). AI Hiring Tools Under Attack: ACLU Files Claims with Feds Over Common Hiring Tools https://www.fisherphillips.com/en/news-insights/ai-hiring-tools-under-attack-aclu-files-claims-with-feds-over-common-hiring-tools.html link

HR Dive (2024, June 5). ACLU asks FTC to probe Aon AI employment assessment tools for bias https://www.hrdive.com/news/aclu-ftc-complaint-aon-ai-employment-assessment-tools-biased/717954/ link

AIAAIC, Arkansas DHS ARChoices RUGs algorithm https://www.aiaaic.org/aiaaic-repository/ai-algorithmic-and-automation-incidents/arkansas-dhs-archoices-rugs-algorithm link

Arkansas Department of Human Services v. Ledgerwood, 2017 Ark. 308, 530 S.W.3d 336 (Ark. 2017) https://www.courtlistener.com/opinion/4441883/ark-dept-of-human-servs-v-ledgerwood/ link

Benefits Tech Advocacy Hub, Arkansas Medicaid HCBS Hours Cuts https://www.btah.org/case-study/arkansas-medicaid-home-and-community-based-services-hours-cuts.html link

Center for Democracy & Technology, When computer programs cut benefits https://cdt.org/insights/what-happens-when-computer-programs-automatically-cut-benefits-that-disabled-people-rely-on-to-survive/ link

Elder v. Gillespie (8th Cir. 2022) https://caselaw.findlaw.com/court/us-8th-circuit/2088858.html link

Just Auto (2018, October 17). Audi develops AI software for quality inspections in press shops. https://www.just-auto.com/news/audi-develops-ai-software-for-quality-inspections-in-press-shops/ link

Lean Enterprise Institute. Automatic Line Stop (Lean Lexicon). https://www.lean.org/lexicon-terms/automatic-line-stop/ link

Law Society Journal, Crude, cruel and unlawful: Robodebt findings https://lsj.com.au/articles/crude-cruel-and-unlawful-robodebt-royal-commission-findings/ link

Royal Commission into the Robodebt Scheme (Wikipedia overview) https://en.wikipedia.org/wiki/Royal_Commission_into_the_Robodebt_Scheme link

Royal Commission into the Robodebt Scheme, Report (2023) https://robodebt.royalcommission.gov.au/publications/report link

Commonwealth Ombudsman, Automation in the Targeted Compliance Framework (2025) https://www.ombudsman.gov.au/__data/assets/pdf_file/0017/320750/Automation-in-the-Targeted-Compliance-Framework.pdf link

Commonwealth Ombudsman, Fairness in the Targeted Compliance Framework (2025) https://www.ombudsman.gov.au/__data/assets/pdf_file/0015/323205/Fairness-in-the-Targeted-Compliance-Framework.pdf link

Department of Employment and Workplace Relations, An update on the Targeted Compliance Framework (2025) https://www.dewr.gov.au/assuring-integrity-targeted-compliance-framework/announcements/update-targeted-compliance-framework link

Department of Employment and Workplace Relations, Targeted Compliance Framework Assurance Review: Final Report (Deloitte assurance review) (2025) https://www.dewr.gov.au/assuring-integrity-targeted-compliance-framework/resources/targeted-compliance-framework-assurance-review-final-report link

Department of Social Services, Social Security Guide 3.11.13: Targeted Compliance Framework (2025) https://guides.dss.gov.au/social-security-guide/3/11/13/10 link

House Select Committee on Workforce Australia Employment Services, Rebuilding Employment Services: Final Report (2023) https://www.aph.gov.au/Parliamentary_Business/Committees/House/Former_Committees/Workforce_Australia_Employment_Services/WorkforceAustralia/Report link

Information Age (Australian Computer Society), Not lawful: government system cancelled 1,009 job seekers benefits (2025) https://ia.acs.org.au/article/2025/-not-lawful---govt-system-cancelled-1-009-job-seekers--benefits.html link

iTnews, Job seekers had payments cancelled unlawfully by government IT system (2025) https://www.itnews.com.au/news/job-seekers-had-payments-cancelled-unlawfully-by-gov-it-system-619327 link

Power to Persuade, Another big little Targeted Compliance Framework crisis you might not have heard of yet (2025) https://www.powertopersuade.org.au/blog/another-big-little-targeted-compliance-framework-issue-you-might-not-have-heard-of-yet/9/7/2025 link

SBS News, Workforce Australia jobseeker changes explained: what is changing and what is not (2025) https://www.sbs.com.au/news/article/workforce-australia-jobseeker-changes-explained/2a5briukk link

The Antipoverty Centre, Potentially 100,000-plus unlawful Centrelink payment cancellations, DEWR admits in estimates hearing (2026) https://apcentre.substack.com/p/potentially-100000-unlawful-centrelink link

The Examiner (Australian Associated Press), Unlawful welfare cancellations: Ombudsman report findings (2025) https://www.examiner.com.au/story/9033710/unlawful-welfare-cancellations-ombudsman-report-findings/ link

The Mandarin, Robodole, another gift that keeps on giving (2025) https://www.themandarin.com.au/298467-robodole-another-gift-that-keeps-on-giving/ link

Lulamae, J. (2022, September 5). The BAMF's controversial dialect recognition software: new languages and an EU pilot project. AlgorithmWatch https://algorithmwatch.org/en/bamf-dialect-recognition/ link

Scheel, S. (2024). Epistemic domination by data extraction: questioning the use of biometrics and mobile phone data analysis in asylum procedures. Journal of Ethnic and Migration Studies, 50(9), 2289-2308 https://pmc.ncbi.nlm.nih.gov/articles/PMC11034547/ link

Magic Notes (Beam) product / methodology https://magicnotes.ai/ link

SocialCare.Today, Magic Notes AI saves social workers time (human-in-the-loop) https://socialcare.today/2024/09/26/magic-notes-ai-tool-saves-social-workers-time-on-admin/ link

Somerset Council, social workers save time with Magic Notes https://www.somerset.gov.uk/news/somerset-social-workers-save-time-on-admin-thanks-to-ai-tool-magic-notes/ link

Brubaker, Benefits Data Trust is shutting down in 60 days (The Philadelphia Inquirer, 2024) https://www.inquirer.com/health/benefits-data-trust-bdt-shutting-down-20240625.html link

Brubaker, Benefits Data Trust is leaving employees and supporters in the dark over its abrupt closure (The Philadelphia Inquirer, 2024) https://www.inquirer.com/health/benefits-data-trust-bdt-surprise-closure-philadelphia-20240627.html link

Brubaker, Benefits Data Trust failed to find a partner to take over its work, will close Aug. 24 (The Philadelphia Inquirer, 2024) https://www.inquirer.com/health/benefits-data-trust-bdt-failed-acquirer-closing-august-24-20240730.html link

Brubaker, What the loss of Benefits Data Trust means for two government agencies in Harrisburg and Philly (The Philadelphia Inquirer, 2024) https://www.inquirer.com/health/benefits-data-trust-closing-august-23-20240823.html link

Burnley, After the abrupt closure of Benefits Data Trust, Philly nonprofits are stepping up to fill in the gaps (Technical.ly and The Philadelphia Citizen, 2024) https://technical.ly/civic-news/philadelphia-senior-care-benefits-navigation/ link

Mosbrucker-Garza, Philly's Benefits Data Trust shutters after 20 years. Laid-off workers say they still want answers (WHYY News, 2024) https://whyy.org/articles/philadelphia-benefits-data-trust-closure-employees-laid-off/ link

Romens, Benefits Data Trust's closure should prompt us to rebuild the flawed public benefits system (The Philadelphia Inquirer and The Pew Charitable Trusts, opinion, 2024) https://www.inquirer.com/opinion/commentary/benefits-bdt-pew-20240806.html link

Wink, Why Benefits Data Trust fell apart despite millions from philanthropy and government contracts (Technical.ly, 2024) https://technical.ly/civic-news/benefits-data-trust-shutdown-trooper-sanders/ link

Teale, A nonprofit's abrupt closure puts access to public benefits at risk (Route Fifty, 2024) https://www.route-fifty.com/management/2024/07/nonprofits-abrupt-closure-puts-access-public-benefits-risk/397968/ link

Lean Enterprise Institute. Automatic Line Stop (Lean Lexicon). https://www.lean.org/lexicon-terms/automatic-line-stop/ link

BMW Group PressClub (2025, April 28). Artificial intelligence as a quality booster (GenAI4Q pilot, Plant Regensburg). https://www.press.bmwgroup.com/global/article/detail/T0449729EN/artificial-intelligence-as-a-quality-booster?language=en link

Metrology and Quality News (2026, July 6). BMW Group Advances Use of Physical AI in Production (AIQX, Plant Spartanburg). https://metrology.news/bmw-group-advances-use-of-physical-ai-in-production/ link

CNN Brasil, App do INSS esbarra em analfabetismo digital de idosos (2025) https://www.cnnbrasil.com.br/blogs/luisa-martins/politica/app-do-inss-esbarra-em-analfabetismo-digital-de-idosos/ link

Conexao Trabalho Portal da Industria CNI, Medida Provisoria limita prazo de duracao de beneficios concedidos por analise documental (2025) https://conexaotrabalho.portaldaindustria.com.br/noticias/detalhe/previdencia/ageral/medida-provisoria-limita-prazo-de-duracao-de-beneficios-concedidos-por-analise-documental/ link

Conselho Nacional de Justica, Justica em Numeros painel previdenciario (2024) https://www.cnj.jus.br/pesquisas-judiciarias/justica-em-numeros/ link

Consultor Juridico, INSS alcanca a marca de 5 milhoes de processos em andamento diz CNJ (2024) https://www.conjur.com.br/2024-dez-02/inss-alcanca-a-marca-de-5-milhoes-de-processos-em-andamento-diz-cnj/ link

Consultor Juridico, INSS nega beneficios injustamente e prejudica milhares de segurados (2025) https://www.conjur.com.br/2025-abr-07/inss-nega-beneficios-injustamente-e-prejudica-milhares-de-segurados/ link

FDR, Governo economiza R$ 2,4 bilhoes apos pente-fino dos auxilios-doenca do INSS (2025) https://fdr.com.br/2025/03/11/governo-economiza-r-24-bilhoes-apos-pentefino-dos-auxiliosdoenca-do-inss-veja-como-escapar/ link

InfoMoney, INSS erra em mais de 10 por cento dos beneficios negados aponta TCU (2025) https://www.infomoney.com.br/minhas-financas/inss-erra-em-mais-de-10-dos-beneficios-negados-aponta-tcu/ link

Jornal de Brasilia, TCU determina que INSS mude sistema de concessao automatica de aposentadorias (2026) https://jornaldebrasilia.com.br/noticias/economia/tcu-determina-que-inss-mude-sistema-de-concessao-automatica-de-aposentadorias-entenda/ link

Observatorio de Politica Fiscal FGV IBRE, A Chamativa Evolucao das Concessoes de Beneficios no INSS e o Atestmed (Rogerio Nagamine Costanzi) (2026) https://observatorio-politica-fiscal.ibre.fgv.br/politica-economica/outros/chamativa-evolucao-das-concessoes-de-beneficios-no-inss-e-o-atestmed link

Previdenciarista, Pente-fino do INSS corta mais da metade dos auxilios-doenca (2025) https://previdenciarista.com/blog/pente-fino-do-inss-corta-mais-da-metade-dos-auxilios-doenca/ link

Tribunal de Contas da Uniao, TCU analisa indeferimentos indevidos no INSS (Acordao 634/2025-Plenario, TC 008.309/2024-8) (2025) https://portal.tcu.gov.br/imprensa/noticias/tcu-analisa-indeferimentos-indevidos-no-inss link

Alishani, Homburg, When citizens meet the chatbot: Evidence from a survey vignette experiment in Estonia (Public Policy and Administration, 2025) https://journals.sagepub.com/doi/10.1177/09520767251404286 link

e-Estonia (Enterprise Estonia briefing centre), Estonia's new virtual assistant aims to rewrite the way people interact with public services (2022) https://e-estonia.com/estonias-new-virtual-assistant-aims-to-rewrite-the-way-people-interact-with-public-services/ link

European Commission, Interoperable Europe / Open Source Observatory (OSOR), Digital public services based on open source: case study on Burokratt (2022) https://interoperable-europe.ec.europa.eu/collection/open-source-observatory-osor/document/digital-public-services-based-open-source-case-study-burokratt link

European Commission, Interoperable Europe Portal (Public Sector Tech Watch), Burokratt: a single chatbot for Estonia (2025) https://interoperable-europe.ec.europa.eu/collection/public-sector-tech-watch/burokratt-single-chatbot-estonia link

European Commission, Recovery and Resilience Facility, Burokratt programme and national virtual assistant platform and ecosystem (2025) https://reforms-investments.ec.europa.eu/projects/burokratt-programme-and-national-virtual-assistant-platform-and-ecosystem_en link

GovInsider, Estonia eyes cross-border interoperability for Burokratt, its Siri of public services (2025) https://govinsider.asia/intl-en/article/estonia-eyes-cross-border-interoperability-for-burokratt-its-siri-of-public-services link

Information System Authority (RIA) / Burokratt open-source project, buerokratt GitHub organisation (2026) https://github.com/buerokratt link

Information System Authority (RIA), Republic of Estonia, Burokratt (2025) https://www.ria.ee/en/state-information-system/personal-services/burokratt link

Information System Authority (RIA), Republic of Estonia, Burokratt citizen-facing portal (2025) https://buerokratt.ee/ link

Kaun, Manniste, Public sector chatbots: AI frictions and data infrastructures at the interface of the digital welfare state (New Media and Society, 2025) https://journals.sagepub.com/doi/10.1177/14614448251314394 link

Kratid.ee (Estonian Ministry of Justice and Digital Affairs, national AI programme), Burokratt (2025) https://www.kratid.ee/en/burokratt link

Paperjam (Luxembourg), Burokratt: Estonia's chatbot network that Luxembourg could adopt (2024) https://en.paperjam.lu/article/burokratt-estonia-s-chatbot-ne link

Stanford Legal Design Lab / Justice Innovation, How AI is augmenting human-led legal advice at Citizens Advice (Caddy adviser copilot) https://justiceinnovation.law.stanford.edu/how-ai-is-augmenting-human-led-legal-advice-at-citizens-advice/ link

Computing, Why Citizens Advice built a chatbot then made sure citizens could not use it (interview with Stuart Pearson, CASORT, 2025) https://www.computing.co.uk/interview/2025/why-citizens-advice-built-a-chatbot link

Department for Science, Innovation and Technology / i.AI / CASORT, Caddy (AI Knowledge Hub use case, 2025) https://ai.gov.uk/knowledge-hub/use-cases/caddy/ link

Fitzgerald, Building Caddy, an AI support tool for adviser teams (Scottish Council for Voluntary Organisations, 2025) https://scvo.scot/p/97562/2025/03/10/building-caddy-an-ai-support-tool-for-advisor-teams link

Incubator for AI (i.AI), i-dot-ai/caddy-chatbot (source repository, MIT licence, archived 2025) https://github.com/i-dot-ai/caddy-chatbot link

Say, i.AI and Citizens Advice develop AI assistant (UKAuthority, 2024) https://www.ukauthority.com/articles/iai-and-citizens-advice-develop-ai-assistant/ link

Say, Citizens Advice SORT to launch Caddy 2.0 (UKAuthority, 2025) https://www.ukauthority.com/articles/citizens-advice-sort-to-launch-caddy-20 link

Stanford Legal Design Lab, Caddy Q and A copilot (JusticeBench project page, 2025) https://www.justicebench.org/project/caddy link

Varotsis, Transforming Civic Engagement with Caddy (Incubator for Artificial Intelligence, i.AI, UK Government, developer blog, 2025) https://ai.gov.uk/blogs/transforming-civic-engagement-with-caddy/ link

Incubator for Artificial Intelligence (i.AI, UK Government), Frontline Services, Caddy (programme page, 2026) https://ai.gov.uk/our-work/frontline-services/ link

A Rule Search Framework for the Early Identification of Chronic Emergency Homeless Shelter Clients (arXiv:2205.09883, 2022, v3 2023) https://arxiv.org/abs/2205.09883 link

Calgary Drop-In Centre (official website, accessed 2026) https://calgarydropin.ca/ link

Geoff Messier research group, University of Calgary (lab website, accessed 2026) https://www.lib.engineer/ link

Masrani, Messier, Voida, Dimitropoulos, He, Understanding Data Usage when Making High-Stakes Frontline Decisions in Homelessness Services (arXiv:2510.14141, 2025) https://arxiv.org/abs/2510.14141 link

Messier, Tutty, John, The Best Thresholds for Rapid Identification of Episodic and Chronic Homeless Shelter Use (arXiv:2105.01042 full text, 2021, v3 2023) https://arxiv.org/abs/2105.01042 link

Messier, Tutty, John, The Best Thresholds for Rapid Identification of Episodic and Chronic Homeless Shelter Use (International Journal on Homelessness, vol 2 no 1, 2022) https://ojs.lib.uwo.ca/index.php/ijoh/article/view/13607 link

Predicting Chronic Homelessness: The Importance of Comparing Algorithms using Client Histories (Journal of Technology in Human Services, vol 40 no 2, pp. 122-133, 2022; arXiv:2105.15080) https://arxiv.org/abs/2105.15080 link

The Human Behind the Data: Reflections from an Ongoing Co-Design and Deployment of a Data-Navigation Interface for Front-Line Emergency Housing Shelter Staff (CHI 2023 Extended Abstracts, ACM, pp. 1-7) https://arxiv.org/abs/2310.13795 link

California Department of Tax and Fee Administration, News Release 24-02: Leveraging GenAI to Enhance Services for Taxpayers (2024) https://cdtfa.ca.gov/news/24-02.htm link

California Department of Tax and Fee Administration, News Release 25-04: California Moving Forward with Generative Artificial Intelligence in State Call Center (2025) https://cdtfa.ca.gov/news/25-04.htm link

CalMatters, California plans to use AI to answer your tax questions (2024) https://calmatters.org/economy/technology/2024/02/cdtfa-generative-ai/ link

Government Technology Industry Insider California, CDTFA Moves GenAI Assistant Into Call Center Production Environment (2025) https://insider.govtech.com/california/news/cdtfa-moves-genai-assistant-into-call-center-production-environment link

Melhado, CA agencies discontinue some AI projects aimed at making government more efficient (The Sacramento Bee via Yahoo News) (2026) https://www.yahoo.com/news/articles/ca-agencies-discontinue-ai-projects-120000898.html link

Office of the Governor of California, Executive Order N-12-23: Generative Artificial Intelligence (2023) https://www.gov.ca.gov/wp-content/uploads/2023/09/AI-EO-No.12-_-GGN-Signed.pdf link

Office of the Governor of California, Governor Newsom deploys first-in-the-nation GenAI technologies to improve efficiency in state government (2025) https://www.gov.ca.gov/2025/04/29/governor-newsom-deploys-first-in-the-nation-genai-technologies-to-improve-efficiency-in-state-government/ link

State of California GenAI portal (genai.ca.gov), California signs partnerships to utilize GenAI (2024) https://www.genai.ca.gov/2024/05/09/california-signs-partnerships-to-utilize-genai/ link

State of California GenAI portal (genai.ca.gov), Call center productivity project (2024) https://www.genai.ca.gov/ca-action/projects/call-center-productivity/ link

SymSoft Solutions via Business Wire, SymSoft Solutions Powers California's GenAI Revolution with Axyom Assist at CDTFA (press release) (2025) https://www.businesswire.com/news/home/20250514665066/en/SymSoft-Solutions-Powers-Californias-GenAI-Revolution-with-Axyom-Assist-at-CDTFA link

Tax Notes, California Tax Department's AI Project Hindered by Human Error (2024) https://www.taxnotes.com/featured-news/california-tax-departments-ai-project-hindered-human-error/2024/12/02/7nh6y link

California Employment Development Department, Amazon Web Services Collaborates with the EDD to Improve Customer Service for Californians (2023) https://edd.ca.gov/en/newsroom/benefitting-californians/2023/amazon-web-services-collaborates-with-the-edd-to-improve-customer-service-for-californians/ link

California Employment Development Department, California EDD Celebrates International Customer Experience Day by Highlighting Upgrades That Put Customers First (2025) https://edd.ca.gov/en/about_edd/news_releases_and_announcements/california-edd-celebrates-international-customer-experience-day-by-highlighting-upgrades-that-put-customers-first/ link

California Employment Development Department, EDD Modernization Update, July 30, 2025: Recent Customer Service Improvements (2025) https://edd.ca.gov/siteassets/files/pdf_pub_ctr/edd-modernization-update_july-2025.pdf link

California Employment Development Department, EDD's Virtual Assistant (Chatbot) Now Available in Top Eight Languages (2025) https://edd.ca.gov/en/newsroom/benefitting-californians/2025/edds-virtual-assistant-chatbot-now-available-in-top-eight-languages/ link

California Employment Development Department, Smarter Service: Inside EDD's Contact Center Modernization (2025) https://edd.ca.gov/en/newsroom/benefitting-californians/2025/smarter-service-inside-edds-contact-center-modernization/ link

California Employment Development Department, Unemployment Customers Can Chat Online with a Live Agent (2025) https://edd.ca.gov/en/newsroom/benefitting-californians/2025/unemployment-customers-can-chat-online-with-a-live-agent/ link

California Employment Development Department, Unemployment Customers Can Now Get Claim Information Through Chat (2026) https://edd.ca.gov/en/newsroom/benefitting-californians/benefiting-californians-2026/unemployment-customers-can-now-get-claim-information-through-chat/ link

California Legislative Analyst's Office, Overview of Efforts to Modernize EDD's Benefit Systems (Assembly Budget Subcommittee No. 5 handout) (2026) https://lao.ca.gov/handouts/revtax/2026/Overview-of-Efforts-to-Modernize-EDD-Systems-022426.pdf link

California Legislative Analyst's Office, The 2025-26 Budget: EDDNext (2025) https://lao.ca.gov/Publications/Report/4985 link

California Legislative Analyst's Office, The 2025-26 California Spending Plan: Other Provisions (2025) https://lao.ca.gov/Publications/Report/5081 link

California Legislative Analyst's Office, The 2026-27 Budget: Overview of the Governor's Budget (2026) https://lao.ca.gov/Publications/Report/5101 link

California State Senate, Senate Budget and Fiscal Review Subcommittee No. 5 Hearing Agenda, April 23, 2026 (Issue 1: EDDNext Modernization) (2026) https://sbud.senate.ca.gov/system/files/2026-04/sub-5-4.23.26-hearing-agenda-final.pdf link

Hepler, California's billion-dollar bet on EDDNext unemployment reform (CalMatters) (2023) https://calmatters.org/economy/2023/11/california-unemployment-reform-eddnext/ link

InterVision Systems, InterVision's Contact Center Services Have Transformed California's EDD (vendor blog) (2025) https://intervision.com/blog-contact-center-edd/ link

Noone, LAO Recommends Continued Funding for EDDNext with New Guardrails (Government Technology Industry Insider California) (2025) https://insider.govtech.com/california/news/lao-recommends-continued-funding-for-eddnext-with-new-guardrails link

City of London Municipal Artificial Intelligence Applications Lab, HIFIS-model source code and documentation (GitHub, MIT license, 2020) https://github.com/aildnont/HIFIS-model link

Govlaunch Stories, London, ON Uses AI to Fight Chronic Homelessness (2020) https://govlaunch.com/stories/london-on-uses-ai-to-fight-chronic-homelessness link

Lamberink, A City Plagued by Homelessness Builds AI Tool to Predict Who's at Risk (CBC News London, 2020) https://www.cbc.ca/news/canada/london/artificial-intelligence-london-1.5684788 link

LeBel, How One Ontario City Is Blazing the Trail for Public Sector AI Use (Global News, 2023) https://globalnews.ca/news/9765050/london-ontario-artificial-intelligence-homelessness/ link

Redden, Stark, Centivany, Lizotte, Adler, Situating London's AI Homelessness Model (Starling Centre for Just Technologies, Just Societies, Western University, ongoing; accessed 2026) https://starlingcentre.ca/project/situating-londons-ai-homelessness-model/ link

The Canadian Press, Ottawa Latest City to Turn to AI to Predict Chronic Homelessness (CTV News, 2024) https://www.ctvnews.ca/ottawa/article/ottawa-latest-city-to-turn-to-ai-to-predict-chronic-homelessness link

VanBerlo, Ross, Rivard, Booker, Interpretable Machine Learning Approaches to Prediction of Chronic Homelessness (arXiv:2009.09072 preprint, 2020) https://arxiv.org/abs/2009.09072 link

Wray, Explainable AI Predicts Homelessness in Ontario City (Cities Today, 2020) https://cities-today.com/explainable-ai-predicts-homelessness-in-ontario-city/ link

Federal Trade Commission, FTC Launches Inquiry into AI Chatbots Acting as Companions (2025) https://www.ftc.gov/news-events/news/press-releases/2025/09/ftc-launches-inquiry-ai-chatbots-acting-companions link

AI Incident Database, Incident 826: Character.ai Chatbot Allegedly Influenced Teen User Toward Suicide Amid Claims of Missing Guardrails (2024) https://incidentdatabase.ai/cite/826/ link

Axios, Character.AI launches tool for parental insights (2025) https://www.axios.com/2025/03/25/characterai-teen-mental-health-parental-insights link

California Legislative Information, Senate Bill No. 243, Companion chatbots (2025) https://leginfo.legislature.ca.gov/faces/billTextClient.xhtml?bill_id=202520260SB243 link

Character.AI, Community Safety Updates (vendor blog) (2024) https://blog.character.ai/community-safety-updates/ link

Character.AI, Introducing Parental Insights: Enhanced Safety for Teens (vendor blog) (2025) https://blog.character.ai/introducing-parental-insights-enhanced-safety-for-teens/ link

Character.AI, Taking Bold Steps to Keep Teen Users Safe on Character.AI (vendor blog) (2025) https://blog.character.ai/u18-chat-announcement/ link

CNBC, Character.AI to block romantic AI chats for minors a year after teen's suicide (2025) https://www.cnbc.com/2025/10/29/character-ai-chatbots-teens-persona.html link

CNN Business, Character.AI and Google agree to settle lawsuits over teen mental health harms and suicides (2026) https://www.cnn.com/2026/01/07/business/character-ai-google-settle-teen-suicide-lawsuit link

Common Sense Media, Talk, Trust, and Trade-Offs: How and Why Teens Use AI Companions (2025) https://www.commonsensemedia.org/research/talk-trust-and-trade-offs-how-and-why-teens-use-ai-companions link

Courthouse News Service, Florida judge rules AI chatbots not protected by First Amendment (2025) https://www.courthousenews.com/florida-judge-rules-ai-chatbots-not-protected-by-first-amendment/ link

Forbes, Character.ai Enters The Microdrama Race With Characters That Talk Back (2026) https://www.forbes.com/sites/maureenkerr/2026/07/09/characterai-enters-the-microdrama-race-with-characters-that-talk-back/ link

Fox News, Senate panel unanimously passes Hawley's GUARD Act on AI chatbots (2026) https://www.foxnews.com/politics/hawley-champions-guard-act-heartbroken-families-say-ai-chatbots-allegedly-pushed-teens-self-harm link

Futurism, After Teen's Suicide, Character.AI Is Still Hosting Dozens of Suicide-Themed Chatbots (2024) https://futurism.com/suicide-chatbots-character-ai link

NBC News, Lawsuit claims Character.AI is responsible for teen's suicide (2024) https://www.nbcnews.com/tech/characterai-lawsuit-florida-teen-death-rcna176791 link

NPR, Lawsuit: A chatbot hinted a kid should kill his parents over screen time limits (2024) https://www.npr.org/2024/12/10/nx-s1-5222574/kids-character-ai-lawsuit link

PYMNTS, Google Reportedly Spent 2.7 Billion to Rehire Character.AI Founder (2024) https://www.pymnts.com/news/artificial-intelligence/2024/google-reportedly-spent-2-7-billion-to-rehire-character-ai-founder/ link

Reason (Volokh Conspiracy), Court Allows Lawsuit Over Character.AI Conversations That Allegedly Caused 14-Year-Old's Suicide to Go Forward (2025) https://reason.com/volokh/2025/05/21/court-allows-lawsuit-over-character-ai-conversations-that-allegedly-caused-14-year-olds-suicide-to-go-forward/ link

Texas Office of the Attorney General, Attorney General Ken Paxton Investigates Meta and Character.AI for Misleading Children with Deceptive AI-Generated Mental Health Services (2025) https://www.texasattorneygeneral.gov/news/releases/attorney-general-ken-paxton-investigates-meta-and-characterai-misleading-children-deceptive-ai link

Texas Office of the Attorney General, Attorney General Ken Paxton Launches Investigations into Character.AI, Reddit, Instagram, Discord, and Other Companies over Children's Privacy and Safety Practices (2024) https://www.texasattorneygeneral.gov/news/releases/attorney-general-ken-paxton-launches-investigations-characterai-reddit-instagram-discord-and-other link

The Bureau of Investigative Journalism, Gang leaders, school shooters and Bestie Epstein: meet Character.AI's chatbot companions (2025) https://www.thebureauinvestigates.com/stories/2025-10-22/gang-leaders-school-shooters-and-bestie-epstein-meet-character.ais-chatbot-companions link

The Washington Post, Character.ai sued after teen's AI companion suggested killing his parents (2024) https://www.washingtonpost.com/technology/2024/12/10/character-ai-lawsuit-teen-kill-parents-texas/ link

US Senate Judiciary Committee, Testimony of Megan Garcia, Subcommittee on Crime and Counterterrorism (2025) https://www.judiciary.senate.gov/imo/media/doc/e2e8fc50-a9ac-05ec-edd7-277cb0afcdf2/2025-09-16%20PM%20-%20Testimony%20-%20Garcia.pdf link

Federal Trade Commission and Consumer Financial Protection Bureau (2023, October 12). Settlement to Require Trans Union to Pay $15 Million over Charges It Failed to Ensure Accuracy of Tenant Screening Reports (a DIFFERENT product; not about sanctions name screening) https://www.ftc.gov/news-events/news/press-releases/2023/10/ftc-cfpb-settlement-require-trans-union-pay-15-million-over-charges-it-failed-ensure-accuracy-tenant link

Ballard Spahr LLP, Consumer Finance Monitor (2024, January 16). CFPB issues two new FCRA advisory opinions on background screening reports and disclosure of credit files to consumers https://www.consumerfinancemonitor.com/2024/01/16/cfpb-issues-two-new-fcra-advisory-opinions-on-background-screening-reports-and-disclosure-of-credit-files-to-consumers/ link

Checkr, Inc. (2026). Background Check Services for Gig and Marketplace (product page, fetched 2026-08-28); and Checkr, Inc. homepage (140,000-plus business customers) https://checkr.com/use-cases/gig-marketplace link

Class Action Complaint, Golightly v. Uber Technologies, Inc. and Checkr, Inc., No. 1:21-cv-03005 (S.D.N.Y., filed 8 April 2021), hosted by Mobilization for Justice https://mobilizationforjustice.org/wp-content/uploads/Golightly-Uber-and-Checkr-Filed-Complaint.pdf link

Consumer Financial Protection Bureau (2021, November 4). CFPB Takes Action to Stop False Identification by Background Screeners (advisory opinion: Fair Credit Reporting; Name-Only Matching Procedures, 86 FR 62468) https://www.consumerfinance.gov/about-us/newsroom/cfpb-takes-action-to-stop-false-identification-by-background-screeners/ link

Consumer Financial Protection Bureau (2024, January 11). CFPB Addresses Inaccurate Background Check Reports and Sloppy Credit File Sharing Practices (advisory opinions: Background Screening, 89 FR 4171; File Disclosure, 89 FR 4167) https://www.consumerfinance.gov/about-us/newsroom/cfpb-addresses-inaccurate-background-check-reports-and-sloppy-credit-file-sharing-practices/ link

Consumer Financial Protection Bureau (2025, May 12). Withdrawn Guidance (list of guidance documents withdrawn 12 May 2025, including Name-Only Matching 86 FR 62468, Permissible Purposes 87 FR 41243, Background Screening 89 FR 4171 and File Disclosure 89 FR 4167) https://www.consumerfinance.gov/compliance/guidance/withdrawn-guidance/ link

Federal Trade Commission (2023, September 11). FTC Says TruthFinder, Instant Checkmate Deceived Users About Background Report Accuracy, Violated FCRA While Marketing Reports for Employee and Tenant Screening https://www.ftc.gov/news-events/news/press-releases/2023/09/ftc-says-truthfinder-instant-checkmate-deceived-users-about-background-report-accuracy-violated-fcra link

Hunton Andrews Kurth LLP (2021, April 14). Gig Employer Hit with Background Check Class Action (Employment and Labor Perspectives) https://www.hunton.com/hunton-employment-labor-perspectives/gig-employer-hit-with-background-check-class-action link

Mobilization for Justice (2021, April 8). Uber Named in First Class Action Lawsuit Challenging Discrimination Against Drivers with Criminal History and Violations of the Fair Chance Act and Fair Credit Laws https://mobilizationforjustice.org/news-and-press/uber-class-action-lawsuit/ link

Opinion and Order, Golightly v. Uber Technologies, Inc. et al., No. 1:21-cv-03005, Doc. 51 (S.D.N.Y. 21 December 2022) (Liman, J.); host returned HTTP 403 on 2026-08-28 and the order was not read https://law.justia.com/cases/federal/district-courts/new-york/nysdce/1:2021cv03005/557807/51/ link

Top Class Actions (2023). Uber Eats New York background check $3.35M class action settlement (Aguilera v. Uber Technologies Inc. d/b/a Uber Eats, No. 509275/2023, N.Y. Sup. Ct. Kings County) https://topclassactions.com/lawsuit-settlements/closed-settlements/uber-eats-new-york-background-check-3-35m-class-action-settlement/ link

Top Class Actions (2026, January). Class action claims Checkr misreported criminal records in background checks (Davis v. Checkr, Inc., No. 0:26-cv-60088, S.D. Fla., filed 26 January 2026) https://topclassactions.com/lawsuit-settlements/lawsuit-news/class-action-claims-checkr-misreported-criminal-records-in-background-checks/ link

BioBioChile, Gobierno anuncia apertura de Oficinas de Ninez en todas las comunas ante alza de abuso sexual infantil (2025) https://www.biobiochile.cl/noticias/nacional/chile/2025/02/27/gobierno-anuncia-apertura-de-oficinas-de-ninez-en-todas-las-comunas-ante-alza-de-abuso-sexual-infantil.shtml link

Center for Human Rights and Global Justice, NYU School of Law (Victoria Adelmant), Risk Scoring Children in Chile (2022) https://chrgj.org/2022-04-20-risk-scoring-children-in-chile/ link

Centre for Social Data Analytics, Auckland University of Technology, Alerta Ninez Child Welfare Predictive Risk Model (Proof of Concept) (2019) https://csda.aut.ac.nz/research/our-projects/2018/alerta-ninez-child-welfare-predictive-risk-model-proof-of-concept link

Defensoria de la Ninez, Balance inicial a la implementacion de las Oficinas Locales de la Ninez (2025) https://www.defensorianinez.cl/wp-content/uploads/2025/03/Documento-especializado-Balance-inicial-de-la-implementacion-OLN.pdf link

Derechos Digitales (Matias Valderrama), AI and Inclusion: Chile 'The Child Alert System' (2022) https://www.derechosdigitales.org/wp-content/uploads/02_Informe-Chile-EN_180222.pdf link

Derechos Digitales (Matias Valderrama), IA e inclusion: Chile 'Sistema Alerta Ninez' y la prediccion del riesgo de vulneracion de derechos de la infancia (2021) https://www.derechosdigitales.org/wp-content/uploads/CPC_informe_Chile.pdf link

Diario y Radio Universidad de Chile, Alerta Infancia: el software que expone los datos personales de ninos y ninas en riesgo social (2019) https://radio.uchile.cl/2019/01/29/alerta-infancia-el-sofware-que-expone-los-datos-personales-de-ninos-y-ninas-en-riesgo-social/ link

Direccion Nacional del Servicio Civil (Concurso Funciona!), Sistema de Alerta Ninez (2020) https://funciona.serviciocivil.cl/iniciativa/sistema-de-alerta-ninez/ link

Not My AI (Paz Pena), Shielding Neoliberalism: 'Social Acceptability' to Avoid Social Accountability of A.I. (2022) https://notmy.ai/news/case-study-a-childhood-alert-system-sistema-alerta-ninez-san-chile/ link

Consumer Financial Protection Bureau (2024, May 7). Consent Order, In the Matter of Chime Financial, Inc., File No. 2024-CFPB-0002. https://files.consumerfinance.gov/f/documents/cfpb_chime-financial-inc-consent-order_2024-05.pdf link

Kessler, C. (2021, July 6). A Banking App Has Been Suddenly Closing Accounts, Sometimes Not Returning Customers' Money. ProPublica. https://www.propublica.org/article/chime link

Rucker, P., Miller, M., & Armstrong, D. (2023, March 25). How Cigna Saves Millions by Having Its Doctors Reject Claims Without Reading Them. ProPublica, co-published with The Capitol Forum https://www.propublica.org/article/cigna-pxdx-medical-health-insurance-rejection-claims link

California Department of Managed Health Care (2025, October 8). DMHC Fines Cigna HealthCare of California $500,000 for Improperly Denying Health Care Claims (press release) https://www.dmhc.ca.gov/Resources/Newsroom/PressReleases/October8,2025.aspx link

Georgetown Law, O'Neill Institute Health Care Litigation Tracker. Kisting-Leung et al. v. Cigna Corporation et al. (tracker entry, read August 2026) https://litigationtracker.law.georgetown.edu/litigation/kisting-leung-et-al-v-cigna-corporation-et-al/ link

Healthcare Dive (2023). Cigna sued over algorithm allegedly used to deny claims; and Cigna hit with second class action suit over claims automation software https://www.healthcaredive.com/news/cigna-lawsuit-algorithm-claims-denials-california/688857/ link

Kisting-Leung, et al. v. Cigna Corporation, et al., No. 2:23-cv-01477-DAD-CSK (E.D. Cal.), Order Granting in Part and Denying in Part Defendants' Motion to Dismiss, Doc. 55, 31 March 2025 (Drozd, J.) https://litigationtracker.law.georgetown.edu/wp-content/uploads/2023/08/Kisting-Leung-et-al_2025.03.31_ORDER-GRANTING-IN-PART-AND-DENYING-IN-PART-DEFENDANTS-MOTION-TO-DISMISS.pdf link

Kisting-Leung v. Cigna Corp., Joint Stipulation and Proposed Order to Extend Deadlines, Doc. 97, 24 August 2026 https://litigationtracker.law.georgetown.edu/wp-content/uploads/2023/08/Kisting-Leung-et-al-v.-Cigna-Corp_2026.08.24_JOINT-STIPULATION.pdf link

Rucker, P., Miller, M., & Armstrong, D. (2023, May 16). Congressional Committee, Regulators Question Cigna System That Lets Its Doctors Deny Claims Without Reading Patient Files. ProPublica / The Capitol Forum https://www.propublica.org/article/cigna-health-insurance-denials-pxdx-congress-investigation link

Sidney Hillman Foundation (2023, April). The Capitol Forum, ProPublica win April Sidney for Exposing Cigna's Policy of Rejecting Health Insurance Claims Without Reading Them https://hillmanfoundation.org/sidney-awards/capitol-forum-propublica-win-april-sidney-exposing-cignas-policy-rejecting-health link

The Cigna Group Newsroom. PxDx (standing explainer page); and Cigna Newsroom (2023, July 27). Cigna Healthcare Affirms its Approach to Expediting Physician Payments https://newsroom.thecignagroup.com/pxdx link

U.S. House Committee on Energy and Commerce (2023, May 16). E&C Republicans Press Cigna for Clarification After Investigative Report Accuses Insurance Company of Denying Claims Without Reading Them https://energycommerce.house.gov/posts/e-and-c-republicans-press-cigna-for-clarification-after-investigative-report-accuses-insurance-company-of-denying-claims-without-reading-them link

Consumer Financial Protection Bureau (2022). Circular 2022-03: Adverse action notification requirements in connection with credit decisions based on complex algorithms; and (2023) Circular 2023-03: Adverse action notification requirements and the proper use of the CFPB's sample forms provided in Regulation B https://www.consumerfinance.gov/compliance/circulars/circular-2022-03-adverse-action-notification-requirements-in-connection-with-credit-decisions-based-on-complex-algorithms/ link

ABA Banking Journal (2025). CFPB ends consent order against Citibank over Armenian discrimination allegations https://bankingjournal.aba.com/2025/11/cfpb-ends-consent-order-against-citibank-over-armenian-discrimination-allegations/ link

Banking Dive (2025). CFPB ends Citi consent order 3 years early https://www.bankingdive.com/news/cfpb-ends-citi-consent-order-3-years-early-discrimination-armenian-2023/803127/ link

Banking Dive (2026, May 14). CFPB's Vought defends terminating Citi consent order https://www.bankingdive.com/news/cfpb-vought-defends-terminating-citi-consent-order-armenian-american-discrimination/820220/ link

ClassAction.org (2023). Class Action Alleges Citibank Intentionally Discriminates Against Consumers of Armenian Descent (Smbatian et al. v. Citibank, N.A. et al., 2:23-cv-09811) https://www.classaction.org/news/class-action-alleges-citibank-intentionally-discriminates-against-consumers-of-armenian-descent link

Consent Order, In the Matter of Citibank, N.A., File No. 2023-CFPB-0013 (issued 8 November 2023; stipulation executed 3 November 2023) https://files.consumerfinance.gov/f/documents/cfpb_citibank-na_2023-cfpb-0013_consent-order_2023-11.pdf link

Consumer Financial Protection Bureau (2023, November 8). CFPB Orders Citi to Pay $25.9 Million for Intentional, Illegal Discrimination Against Armenian Americans (press release; body text since archived off the live page; capture of 10 November 2023: https://web.archive.org/web/20231110171559/https://www.consumerfinance.gov/about-us/newsroom/cfpb-orders-citi-to-pay-25-9-million-for-intentional-illegal-discrimination-against-armenian-americans/) link

Consumer Financial Protection Bureau (2024, June). Fair Lending Report of the Consumer Financial Protection Bureau, covering fiscal year 2023 https://files.consumerfinance.gov/f/documents/cfpb_fair-lending-report_fy-2023.pdf link

Daily Journal (2024). Citibank's arbitration clause challenged in Armenian bias case (16 April 2024); and Judge approves arbitration in Citibank Armenian bias case (18 April 2024) https://www.dailyjournal.com/articles/378045-citibank-s-arbitration-clause-challenged-in-armenian-bias-case link

Enforcement action page: Citibank, N.A. (2023-CFPB-0013) https://www.consumerfinance.gov/enforcement/actions/citibank-n-a/ link

Los Angeles Times (2025, March 11), accessed via Yahoo News syndication. L.A. investigating banking discrimination complaints against Armenians https://www.yahoo.com/news/l-investigating-banking-discrimination-complaints-220904915.html link

Office of U.S. Senator Adam Schiff (2025, October 17). Statement: Sen. Schiff Blasts Trump Administration's Free Pass to Citibank for Armenian Discrimination Through Termination of Case Settlement https://www.schiff.senate.gov/news/press-releases/statement-sen-schiff-blasts-trump-administrations-free-pass-to-citibank-for-armenian-discrimination-through-termination-of-case-settlement link

Office of U.S. Senator Adam Schiff (2026, April 23). Sen. Schiff, Colleagues Probe Consumer Financial Protection Bureau's Termination of Citibank's $25 Million Settlement for Discriminating Against Armenian Americans https://www.schiff.senate.gov/news/press-releases/news-sen-schiff-colleagues-probe-consumer-financial-protection-bureaus-termination-of-citibanks-25-million-settlement-for-discriminating-against/ link

Order Terminating the Consent Order, In the Matter of Citibank, N.A., File No. 2023-CFPB-0013, Document 3 (filed 16 October 2025) https://files.consumerfinance.gov/f/documents/cfpb_citibank-n-a_termination-consent-order_2025-10.pdf link

The Armenian Weekly (2023, November 14). Citibank fined for illegal discrimination against Armenian Americans https://armenianweekly.com/2023/11/14/citibank-fined-for-illegal-discrimination-against-armenian-americans/ link

Yahoo Finance (2026), syndicating Banking Dive. CFPB's Vought defends terminating Citi consent order https://finance.yahoo.com/economy/policy/articles/cfpb-vought-defends-terminating-citi-085231077.html link

Ogletree v. Cleveland State University, No. 1:21-cv-00500 (N.D. Ohio, August 22, 2022); via Higher Ed Dive and Future of Privacy Forum analyses. https://www.highereddive.com/news/test-proctoring-room-scans-violated-college-students-privacy-judge-rules/630340/ link

Yoder-Himes, D.R., Asif, A., Kinney, K., et al. (2022). Racial, skin tone, and sex disparities in automated proctoring software. Frontiers in Education, 7, 881449 https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2022.881449/full link

Banerjee, I., Bhimireddy, A.R., Burns, J.L., et al., and Gichoya, J.W. (2021). Reading Race: AI Recognises Patient's Racial Identity In Medical Images. arXiv:2107.10356; published as Gichoya, J.W., et al. (2022), AI recognition of patient race in medical imaging: a modelling study, The Lancet Digital Health, 4(6) https://arxiv.org/abs/2107.10356 link

American Health Information Management Association (AHIMA). Governance and AI: Putting Rules in Place is Critical (accessed 2026-08-06) https://www.ahima.org/education-events/artificial-intelligence/governance-and-ai-putting-rules-in-place-is-critical/ link

Genes, N., Sills, J., Heaton, H.A., Shy, B.D., & Scofi, J. (2025). Addressing Note Bloat: Solutions for Effective Clinical Documentation. Journal of the American College of Emergency Physicians Open https://pmc.ncbi.nlm.nih.gov/articles/PMC11852943/ link

Guevara, M., Chen, S., Thomas, S., Chaunzwa, T.L., Franco, I., Kann, B.H., Moningi, S., Qian, J.M., Goldstein, M., Harper, S., Aerts, H.J.W.L., Catalano, P.J., Savova, G.K., Mak, R.H., & Bitterman, D.S. (2024). Large language models to identify social determinants of health in electronic health records. npj Digital Medicine, 7, 6 https://pmc.ncbi.nlm.nih.gov/articles/PMC10781957/ link

Ji, M., Xu, S., Yang, F., & Bachman, S.S. (2026). AI in Health Care and Health Disparities. In R. An & M. A. Lindsey (Eds.), Artificial Intelligence in Social Work: Bridging Technology and Humanity. Springer https://doi.org/10.1007/978-3-032-18443-6_5 DOI

National Academies of Sciences, Engineering, and Medicine (2019). Integrating Social Care into the Delivery of Health Care: Moving Upstream to Improve the Nation's Health. Chapter 2, Five Health Care Sector Activities to Better Integrate Social Care. Washington, DC: National Academies Press https://www.ncbi.nlm.nih.gov/books/NBK552600/ link

Palm, K.H., Manikantan, K., Mahal, N., Belwadi, S.K., & Pepin, R.J. (2025). Assessing the quality of AI-generated clinical notes: validated evaluation of a large language model ambient scribe. Frontiers in Artificial Intelligence, 8 https://pmc.ncbi.nlm.nih.gov/articles/PMC12586549/ link

Centers for Medicare & Medicaid Services. "Accountable Health Communities Model" (CMS Innovation Center model page). https://www.cms.gov/priorities/innovation/innovation-models/ahcm link

Johnson, McPeek Hinz, Regan, Nohria, Moon and Spratt, Measures of Referral vs Receipt of Social Services Among Patients With Health-Related Social Needs (JAMA Network Open, 2024) https://pmc.ncbi.nlm.nih.gov/articles/PMC11024758/ link

Sidebottom, Martins, Vacquier, Dechaine and Behrens, Health-Related Social Needs and Health Care Utilization in the Accountable Health Communities Model (JAMA Network Open, 2025) https://pmc.ncbi.nlm.nih.gov/articles/PMC12706677/ link

Zehner, Kirsch, Adsit, Gorrilla, Hayden, Skora, Rosenblum, Baker, Fiore and McCarthy, Electronic health record closed-loop referral (eReferral) to a state tobacco quitline: a retrospective case study of primary care implementation challenges and adaptations (Implementation Science Communications, 2022) https://pmc.ncbi.nlm.nih.gov/articles/PMC9548147/ link

Bonifacic, I. (2023, January 25). CNET had to correct most of its AI-written articles. Engadget. https://www.engadget.com/cnet-corrected-41-of-its-77-ai-written-articles-201519489.html link

Harrison Dupré, M. (2023, November 27). Sports Illustrated Published Articles by Fake, AI-Generated Writers. Futurism. https://futurism.com/sports-illustrated-ai-generated-writers link

Child Protection Ombudsman of Colorado, Independent Audit of Colorado's Family Safety and Risk Assessment Tools, special initiative page (2026) https://coloradocpo.org/special-initiative/independent-audit-of-colorados-family-safety-and-risk-assessment-tools/ link

Colorado Child Welfare Training System (Kempe Center), Colorado Family Safety and Risk Assessments, caseworker resources (2025) https://coloradocwts.com/document-category/caseworker-resources/colorado-family-safety-and-risk-assessments/ link

Colorado Department of Human Services, Differential Response Program (2025) https://cdhs.colorado.gov/differential-response-program link

Colorado General Assembly, HB24-1046: Child Welfare System Tools, bill page (2024) https://leg.colorado.gov/bills/hb24-1046 link

Colorado General Assembly, House Bill 24-1046, enrolled act (2024) https://content.leg.colorado.gov/sites/default/files/documents/2024A/bills/2024a_1046_enr.pdf link

Colorado Office of Respondent Parents' Counsel (host), Colorado Family Safety Assessment Instructions (2016) https://www.coloradoorpc.org/wp-content/uploads/2017/09/CO-Family-Safety-Assessment-Instructions-.pdf link

Colorado Office of the State Auditor, Child Welfare Performance Audit, October 2014 (2014) https://content.leg.colorado.gov/sites/default/files/documents/audits/1303p_-_child_welfare_performance_audit_october_2014_final_rev_11-3-14.pdf link

Colorado Revised Statutes, Section 19-3.3-102: Office of the Child Protection Ombudsman Established, via Justia (2024) https://law.justia.com/codes/colorado/title-19/article-3-3/section-19-3-3-102/ link

ICF Incorporated, Colorado Safety and Risk Assessment Tools Audit: Evaluation of the Colorado Family Risk Assessment and Colorado Family Safety Assessment Tools (for the Office of the Colorado Child Protection Ombudsman, 2026) https://coloradocpo.org/wp-content/uploads/2026/02/Colorado-Family-Safety-Risk-Assessment-Tools-Audit-HB-24-1046_March-2-2026-FINAL-508.pdf link

Office of Colorado's Child Protection Ombudsman, brief to the Colorado Child Welfare System Interim Study Committee, Hearing One, June 27, 2023 (2023) https://content.leg.colorado.gov/sites/default/files/images/office_of_colorados_child_protection_ombudsman_info_brief.pdf link

Steffen, Audit Finds Issues with Colorado's Child Welfare Safety and Risk Assessment Tools (Office of the Colorado Child Protection Ombudsman news release via Pagosa Daily Post, 2026) https://pagosadailypost.com/2026/03/03/audit-finds-issues-with-colorados-child-welfare-safety-and-risk-assessment-tools/ link

Winokur, Ellis, Drury and Rogers, Answering the Big Questions about Differential Response in Colorado: Safety and Cost Outcomes from a Randomized Controlled Trial (Child Abuse and Neglect, published online 2014, journal issue 2015) https://pubmed.ncbi.nlm.nih.gov/24997071/ link

Rainforest Connection, Real-Time Threat Detection (Guardian) (rfcx.org, 2026) https://rfcx.org/guardian link

Rainforest Connection, Biodiversity Monitoring (Arbimon ecoacoustics) (rfcx.org, 2026) https://rfcx.org/ecoacoustics link

Rainforest Connection, Terms of Service (rfcx.org, last updated 5 March 2026) https://rfcx.org/terms-of-service link

Rainforest Connection, Privacy Policy (rfcx.org, 2026) https://rfcx.org/privacy-policy link

World Bank, Rainforest Connection (technology factsheet, thedocs.worldbank.org, 2022; survey vintage 2021-2022) https://thedocs.worldbank.org/en/doc/9006e1b6731d81dd18567415ea871851-0320052022/original/poaching-rainforest-connection.pdf link

Allen, J., Desai, N., Namazi, A., Leas, E., Dredze, M., Smith, D. M., & Ayers, J. W. (2024). Characteristics of X (formerly Twitter) Community Notes addressing COVID-19 vaccine misinformation. JAMA, 331(19), 1670 https://doi.org/10.1001/jama.2024.4800 DOI

Arjmandi-Lari, M., Mantzarlis, A., & Stafford, T. (2025). Threats to the sustainability of Community Notes on X (arXiv:2510.00650, submitted to ICWSM) https://arxiv.org/abs/2510.00650 link

Bell, K. (2023, October 30). X won't pay creators for tweets that get fact checked with community notes. Engadget https://www.engadget.com/x-wont-pay-creators-for-tweets-that-get-fact-checked-with-community-notes-174206477.html link

Center for Countering Digital Hate (2024, October 30). Rated Not Helpful: How X's Community Notes system falls short on misleading election claims (publisher site captcha-blocked; figures carried through Social Media Today's 30 October 2024 write-up at https://www.socialmediatoday.com/news/reports-find-community-notes-failing-address-misinformation-x-formally-twitter/731558/ ) https://counterhate.com/research/rated-not-helpful-x-community-notes/ link

Chuai, Y., et al. (2026). Community-based fact-checking reduces the spread of misleading posts on X (formerly Twitter). Nature Communications, 17, 4070 https://doi.org/10.1038/s41467-026-72597-0 DOI

Chuai, Y., Tian, H., Proferes, N., Zhang, K., & Lenzini, G. (2024). Did the roll-out of Community Notes reduce engagement with misinformation on X/Twitter? Proceedings of the ACM on Human-Computer Interaction, 8, CSCW2, Article 428 https://doi.org/10.1145/3686967 DOI

Mohammadi, S., et al. (2026). From Birdwatch to Community Notes, from Twitter to X: four years of community-based content moderation (arXiv:2510.09585v4) https://arxiv.org/abs/2510.09585 link

Purnell, S. (2026, June 16). AI writers on Community Notes: an evaluation of seven months of data. R Street Institute https://www.rstreet.org/research/ai-writers-on-community-notes-an-evaluation-of-seven-months-of-data/ link

Razuvayevskaya, O., et al. (2025). Timeliness, consensus, and composition of the crowd: Community Notes on X (arXiv:2510.12559) https://arxiv.org/abs/2510.12559 link

Renault, T., Restrepo Amariles, D., & Troussel, A. (2024). Collaboratively adding context to social media posts reduces the sharing of false news (arXiv:2404.02803; HEC Paris Research Paper LAW-2024-1519). Working paper, no journal publication located https://arxiv.org/abs/2404.02803 link

Slaughter, I., Peytavin, A., Ugander, J., & Saveski, M. (2025). Community notes reduce engagement with and diffusion of false information online. Proceedings of the National Academy of Sciences, 122(38), e2503413122 https://arxiv.org/abs/2502.13322 link

TheJournal.ie (2025, May). Community Notes vanishes from X feeds, raising serious questions amid ongoing EU probe https://www.thejournal.ie/x-community-notes-disappeared-from-platform-factchecking-misinformation-elon-musk-6717349-May2025/ link

Truong, B. H., Wu, X., Flammini, A., Menczer, F., & Stewart, A. J. (2025). Community Notes are vulnerable to rater bias and manipulation (arXiv:2511.02615). Agent-based simulation of the published scorer https://arxiv.org/abs/2511.02615 link

X Corp. Community Notes documentation, Under the Hood: Ranking Notes (repository documentation source, Apache-2.0) https://github.com/twitter/communitynotes/blob/main/documentation/under-the-hood/ranking-notes.md link

X Corp. Community Notes documentation, About: FAQ (repository documentation source) https://raw.githubusercontent.com/twitter/communitynotes/main/documentation/about/faq.md link

X Corp. Community Notes documentation, Under the Hood: Download Data (repository documentation source) https://github.com/twitter/communitynotes/blob/main/documentation/under-the-hood/download-data.md link

X Corp. Community Notes documentation, About: Challenges (repository documentation source) https://raw.githubusercontent.com/twitter/communitynotes/main/documentation/about/challenges.md link

X Corp. Community Notes documentation, API overview (automated note-writer pilot) https://raw.githubusercontent.com/twitter/communitynotes/main/documentation/api/overview.md link

European Commission (2023, December 18). Commission opens formal proceedings against X under the Digital Services Act (press release) https://digital-strategy.ec.europa.eu/en/news/commission-opens-formal-proceedings-against-x-under-digital-services-act link

European Commission (2025, December 5). Commission fines X EUR 120 million under the Digital Services Act (press release) https://digital-strategy.ec.europa.eu/en/news/commission-fines-x-eu120-million-under-digital-services-act link

Cohen Milstein Sellers and Toll PLLC, Connecticut Fair Housing Center et al. v. CoreLogic Rental Property Solutions case study page (2023) https://www.cohenmilstein.com/case-study/connecticut-fair-housing-center-et-al-v-corelogic-rental-property-solutions/ link

Connecticut Fair Housing Center v. CoreLogic Rental Property Solutions LLC, 478 F. Supp. 3d 259 (D. Conn. 2020), summary judgment opinion https://www.quimbee.com/cases/connecticut-fair-housing-center-v-corelogic-rental-property-solutions-llc link

Connecticut Fair Housing Center v. CoreLogic Rental Property Solutions LLC, No. 3:18-cv-705, 2023 WL 4669482 (D. Conn. July 20, 2023) (Bryant, J.), bench trial decision https://law.justia.com/cases/federal/district-courts/connecticut/ctdce/3:2018cv00705/125021/317/ link

Connecticut Fair Housing Center v. CoreLogic Rental Property Solutions LLC, Nos. 23-1118(L) and 23-1166(XAP) (2d Cir. Feb. 20, 2026) (Menashi, J.), slip opinion https://business.cch.com/BFLD/CFHC-v-CoreLogic-Rental-Prop-2ndCir-02202026.pdf link

Connecticut Fair Housing Center, Landmark Civil Rights Decision, on the March 25, 2019 denial of CoreLogic's motion to dismiss (2019) https://ctfairhousing.org/landmark-civil-rights-decision/ link

CourtListener and the Free Law Project, Docket, Connecticut Fair Housing Ctr v. CoreLogic Rental Property Solutions LLC, No. 3:18-cv-00705 (D. Conn.) (2023) https://www.courtlistener.com/docket/6691528/connecticut-fair-housing-ctr-v-corelogic-rental-property-solutions-llc/ link

FindLaw Caselaw, Connecticut Fair Housing Center v. CoreLogic Rental Property Solutions LLC (2d Cir. 2026), opinion mirror https://caselaw.findlaw.com/court/us-2nd-circuit/118173935.html link

National Consumer Law Center, SafeRent Solutions Accused of Illegally Discriminating Against Black and Hispanic Rental Applicants (2022) https://www.nclc.org/saferent-solution-accused-of-illegally-discriminating-against-black-and-hispanic-rental-applicants/ link

National Housing Law Project, Arroyo v. CoreLogic case page (2018) https://www.nhlp.org/our-initiatives/arroyo-v-corelogic/ link

National Housing Law Project, Federal Court Holds Tenant Screening Services Must Comply with the Fair Housing Act (2019) https://www.nhlp.org/our-initiatives/federal-court-holds-tenant-screening-services-must-comply-with-the-fair-housing-act/ link

National Mortgage Professional, CoreLogic Rebrands Its Rental Property Solutions Division (2016) https://nationalmortgageprofessional.com/news/58930/corelogic-rebrands-its-rental-property-solutions-division link

NBC News (Cyrus Farivar), Tenant Screening Software Faces National Reckoning (2021) https://www.nbcnews.com/tech/tech-news/tenant-screening-software-faces-national-reckoning-n1260975 link

United States Department of Justice Civil Rights Division with the Department of Housing and Urban Development, Brief of the United States as Amicus Curiae, Connecticut Fair Housing Center v. CoreLogic Rental Property Solutions, No. 23-1118 (2d Cir. Nov. 24, 2023) https://www.justice.gov/d9/2023-11/connecticut_fair_housing_center_v._corelogic_rental_property_solutions_no._23-1118_2d_cir._11.24.23.pdf link

45 CFR 92.210, Nondiscrimination in the use of patient care decision support tools (U.S. Department of Health and Human Services, section 1557 implementing regulation) https://www.ecfr.gov/current/title-45/section-92.210 link

Ledford, H. (2019). Millions of black people affected by racial bias in health-care algorithms. Nature (news), 574, 608-609 https://www.nature.com/articles/d41586-019-03228-6 link

National Academies of Sciences, Engineering, and Medicine (2019). Integrating Social Care into the Delivery of Health Care: Moving Upstream to Improve the Nation's Health. Washington, DC: The National Academies Press, DOI 10.17226/25467, chapter 2 https://nap.nationalacademies.org/read/25467/chapter/4 link

New York State Department of Financial Services and New York State Department of Health, joint letter to UnitedHealth Group Incorporated, 25 October 2019 https://www.dfs.ny.gov/reports-and-publications/comment-letters/dfs-doh-joint-letter-uhgi-20191025 link

Obermeyer, Z., Nissan, R., Stern, M., Eaneff, S., Bembeneck, E. J., & Mullainathan, S. (2021, June). Algorithmic Bias Playbook. Center for Applied AI at Chicago Booth https://www.chicagobooth.edu/-/media/project/chicago-booth/centers/caai/docs/algorithmic-bias-playbook-june-2021.pdf link

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science 366(6464): 447-453, DOI 10.1126/science.aax2342 (open deposit, UC Berkeley Previously Published Works; Europe PMC record MED/31649194) https://escholarship.org/uc/item/6h92v832 link

Snowbeck, C. (2019, October 29). NY Regulators Probe for Racial Bias in Health-Care Algorithm. Star Tribune (via Government Technology syndication) https://www.govtech.com/health/NY-Regulators-Probe-for-Racial-Bias-in-Health-Care-Algorithm.html link

Allensworth, E.M., & Easton, J.Q. (2007). What Matters for Staying On-Track and Graduating in Chicago Public Schools. University of Chicago Consortium on School Research. https://consortium.uchicago.edu/publications/what-matters-staying-track-and-graduating-chicago-public-schools-focus-students link

American Banker (2021). Subprime auto lender reaches $27M settlement with Massachusetts AG https://www.americanbanker.com/news/subprime-auto-lender-reaches-27m-settlement-with-massachusetts-ag link

American Banker (2025, April 25). CFPB drops lawsuit against auto lender Credit Acceptance https://www.americanbanker.com/news/cfpb-drops-lawsuit-against-auto-lender-credit-acceptance link

Civil Rights Litigation Clearinghouse, University of Michigan Law School. Consumer Financial Protection Bureau (CFPB) v. Credit Acceptance Corporation, 1:23-cv-00038 (S.D.N.Y.) case page https://clearinghouse.net/case/48237/ link

Complaint, Consumer Financial Protection Bureau and the People of the State of New York v. Credit Acceptance Corporation, No. 1:23-cv-00038 (S.D.N.Y., filed 4 January 2023) https://files.consumerfinance.gov/f/documents/cfpb_credit-acceptance-corporation_complaint_2023-01.pdf link

Credit Acceptance Corporation, Quarterly Report on Form 10-Q for the quarter ended 30 June 2026 (filed 4 August 2026), SEC EDGAR CIK 0000885550 https://www.sec.gov/Archives/edgar/data/885550/000088555026000182/cacc-20260630.htm link

Docket, People of The State of New York v. Credit Acceptance Corporation, No. 1:23-cv-00038 (S.D.N.Y.), CourtListener/RECAP https://www.courtlistener.com/docket/66698748/consumer-financial-protection-bureau-v-credit-acceptance-corporation/ link

Memorandum in Support of Consent Motion by Plaintiff Consumer Financial Protection Bureau to Withdraw as a Plaintiff, ECF 86-1 (S.D.N.Y., filed 24 April 2025) https://storage.courtlistener.com/recap/gov.uscourts.nysd.591873/gov.uscourts.nysd.591873.86.1.pdf link

Memorandum of Law in Support of Credit Acceptance Corporation's Revised Motion to Dismiss, ECF 75 (S.D.N.Y., filed 14 August 2024) https://storage.courtlistener.com/recap/gov.uscourts.nysd.591873/gov.uscourts.nysd.591873.75.0.pdf link

Office of the Massachusetts Attorney General (2021, September 1). In Largest Settlement of Its Kind, AG Healey Secures $27 Million for Thousands of Massachusetts Consumers from Subprime Auto Lender https://www.mass.gov/news/in-largest-settlement-of-its-kind-ag-healey-secures-27-million-for-thousands-of-massachusetts-consumers-from-subprime-auto-lender link

Office of the New York State Attorney General (2023, January 4). Attorney General James and CFPB Sue Auto Lender for Cheating Thousands of New Yorkers https://ag.ny.gov/press-release/2023/attorney-general-james-and-cfpb-sue-auto-lender-cheating-thousands-new-yorkers link

Order dismissing and discontinuing the action, People of the State of New York v. Credit Acceptance Corporation, ECF 94 (S.D.N.Y., 5 June 2026) (Furman, J.) https://storage.courtlistener.com/recap/gov.uscourts.nysd.591873/gov.uscourts.nysd.591873.94.0.pdf link

Eysenbach, Crisis Text Line and Loris.ai Controversy Highlights the Complexity of Informed Consent on the Internet and Data-Sharing Ethics for Machine Learning and Research (Journal of Medical Internet Research, editorial, 2025) https://pmc.ncbi.nlm.nih.gov/articles/PMC11799832/ link

Benton Institute for Broadband and Society, FCC Commissioner Carr Calls for FTC Probe of Crisis Text Line (2022) https://www.benton.org/headlines/fcc-commissioner-carr-calls-ftc-probe-crisis-text-line link

Broadbent, Medina Grespan, Axford et al., A machine learning approach to identifying suicide risk among text-based crisis counseling encounters (Frontiers in Psychiatry, SafeUT, 2023) https://pmc.ncbi.nlm.nih.gov/articles/PMC10076638/ link

Crisis Text Line (Wikipedia, tertiary encyclopedia entry) (2026) https://en.wikipedia.org/wiki/Crisis_Text_Line link

Crisis Text Line, An Update on Data Privacy, Our Community and Our Service (2022) https://www.crisistextline.org/blog/2022/01/31/an-update-on-data-privacy-our-community-and-our-service/ link

Crisis Text Line, Annual Trends (2025) https://www.crisistextline.org/annual-trends/ link

Crisis Text Line, Detecting Crisis: An AI Solution (2018) https://www.crisistextline.org/blog/2018/03/28/detecting-crisis-an-ai-solution/ link

Crisis Text Line, Understanding Suicide Prevention and Active Rescues at Crisis Text Line (2020) https://www.crisistextline.org/blog/2020/01/03/understanding-suicide-prevention-and-active-rescues-at-crisis-text-line/ link

Markkula Center for Applied Ethics, Santa Clara University, Crisis Data: An Ethics Case Study (2022) https://www.scu.edu/ethics/focus-areas/internet-ethics/resources/crisis-data-an-ethics-case-study/ link

Neville, When Help Isn't Fully Human: The Problem of Generative AI in Crisis Support (Just Tech, Social Science Research Council, 2025) https://just-tech.ssrc.org/articles/the-problem-of-generative-ai-in-crisis-support/ link

Reierson, Reform Crisis Text Line (advocacy site) (2022) https://reformcrisistextline.com/ link

Trujillo, Response From Crisis Text Line to Commentary on Protecting User Privacy and Rights in Academic Data-Sharing Partnerships (Journal of Medical Internet Research, 2025) https://pmc.ncbi.nlm.nih.gov/articles/PMC11799801/ link

Maurer, R. (2021). HireVue Discontinues Facial Analysis Screening. SHRM; with HireVue and ORCAA audit announcements (2021). https://www.shrm.org/topics-tools/news/talent-acquisition/hirevue-discontinues-facial-analysis-screening link

Agency Checklists (2025, June 2). Mass. Insurance Employers: A Hidden Trap That Could Cost You $500 Per Applicant https://agencychecklists.com/2025/06/02/mass-insurance-employers-a-hidden-trap-that-could-cost-you-75990/ link

ArentFox Schiff (Davidson, S.) (2024, May 20). Using New Technology to Vet Job Applicants Can Violate the Massachusetts Lie Detector Statute; and New England Biz Law Update (2024, February 23). Federal judge allows lawsuit against CVS to proceed over alleged lie detector use https://www.afslaw.com/perspectives/ai-law-blog/using-new-technology-vet-job-applicants-can-violate-the-massachusetts-lie link

Baker v. CVS Health Corporation, Civil Action No. 23-11483 (D. Mass. 16 February 2024) (Saris, J.), memorandum and order on motions to dismiss https://caselaw.findlaw.com/court/us-dis-crt-d-mas/115835556.html link

Baker v. CVS Health Corporation et al, No. 1:23-cv-11483 (D. Mass.), public docket record via PacerMonitor https://www.pacermonitor.com/public/case/49427403/Baker_v_CVS_Health_Corporation_et_al link

HR Dive (Crist, C.) (2024, July 24). CVS settles lawsuit alleging it used AI 'lie detector' https://www.hrdive.com/news/cvs-settles-lawsuit-over-using-ai-based-lie-detector/722249/ link

Massachusetts General Laws, Part I, Title XXI, Chapter 149, Section 19B (lie detector tests; employment), enacted 1959, notice and private action added 1985 https://malegislature.gov/Laws/GeneralLaws/PartI/TitleXXI/Chapter149/Section19B link

Morgan Lewis LawFlash (Engelman, K. L., Schuster, S., & Perocchi, A. K.) (2025, May 20). Class Action Litigation Over Massachusetts Lie Detector Statute Surges: What Employers Need to Know https://www.morganlewis.com/pubs/2025/05/class-action-litigation-over-massachusetts-lie-detector-statute-surges-what-employers-need-to-know link

The Boston Globe (Johnston, K.) (2023, May 22). A Milton resident's lawsuit against CVS raises questions about the use of AI lie detectors in hiring https://www.boston.com/news/the-boston-globe/2023/05/22/milton-residents-lawsuit-cvs-ai-lie-detectors/ link

Teradata (2017). Danske Bank Fights Fraud with Deep Learning and AI (case study EB9821). https://assets.teradata.com/resourceCenter/downloads/CaseStudies/CaseStudy_EB9821_Danske_Bank_Saves_Millions_Fighting_Fraud_With_Deep_Learning_and_AI.pdf link

UK Payment Systems Regulator (2023-2025). APP fraud performance data / APP scams performance reports. https://www.psr.org.uk/information-for-consumers/app-fraud-performance-data/ link

Groenfeldt, T. (2017, October 30). Danske Bank Uses Tech To Prevent Digital Fraud. Forbes https://www.forbes.com/sites/tomgroenfeldt/2017/10/30/danske-bank-uses-tech-to-prevent-digital-fraud/ link

Answer to Amended Complaint for Permanent Injunction, Monetary Judgment, Civil Penalty Judgment, and Other Relief, United States v. Dave, Inc., No. 2:24-cv-09566-MRA-AGR (C.D. Cal., 10 October 2025), ECF 80 https://storage.courtlistener.com/recap/gov.uscourts.cacd.947304/gov.uscourts.cacd.947304.80.0.pdf link

Banking Dive (2025). Fintech Dave alters fee structure, blasts DOJ over amended lawsuit https://www.bankingdive.com/news/fintech-dave-alters-fee-structure-blasts-doj-over-amended-lawsuit-jason-wilk/736830/ link

Civil Minutes - General: (In Chambers) Order Denying Defendants' Motion to Dismiss, United States v. Dave, Inc., No. 2:24-cv-09566-MRA-AGR (C.D. Cal., 12 September 2025), ECF 75 https://storage.courtlistener.com/recap/gov.uscourts.cacd.947304/gov.uscourts.cacd.947304.75.0.pdf link

Complaint for Permanent Injunction, Monetary Judgment, and Other Relief (unredacted, unsealed by court order), FTC v. Dave, Inc., No. 2:24-cv-09566 (C.D. Cal., filed 5 November 2024), ECF 24 https://storage.courtlistener.com/recap/gov.uscourts.cacd.947304/gov.uscourts.cacd.947304.24.0.pdf link

Dave Inc. (2025, September 10). Dave Introduces CashAI v5.5 (press release) https://dave.com/press/news-releases/news-release-details/dave-introduces-cashai-v55 link

Dave Inc. (2026, March 2). Annual Report on Form 10-K for the fiscal year ended 31 December 2025, filed with the U.S. Securities and Exchange Commission https://www.sec.gov/Archives/edgar/data/1841408/000119312526085370/dave-20251231.htm link

Dave Inc. (2026, August 5). Quarterly Report on Form 10-Q for the quarterly period ended 30 June 2026, filed with the U.S. Securities and Exchange Commission https://www.sec.gov/Archives/edgar/data/1841408/000119312526335154/dave-20260630.htm link

Dave Inc. (2026). Consumer marketing site, homepage and ExtraCash disclosures as displayed 28 August 2026 https://dave.com/ link

Docket, Federal Trade Commission v. Dave, Inc., No. 2:24-cv-09566 (C.D. Cal.), entries 1-145 (5 November 2024 to 6 August 2026), CourtListener / RECAP Archive https://www.courtlistener.com/docket/69345740/federal-trade-commission-v-dave-inc/ link

Federal Trade Commission (2024, November 5). FTC Takes Action Against Online Cash Advance App Dave for Deceiving Consumers, Charging Undisclosed Fees; and (2024, December 30) FTC Refers Case Against Online Cash Advance Firm Dave Inc. to Department of Justice https://www.ftc.gov/news-events/news/press-releases/2024/11/ftc-takes-action-against-online-cash-advance-app-dave-deceiving-consumers-charging-undisclosed-fees link

First Amended Complaint for Permanent Injunction, Monetary Judgment, Civil Penalty Judgment, and Other Relief, United States v. Dave, Inc. and Jason Wilk, No. 2:24-cv-09566-MRA-AGR (C.D. Cal., filed 30 December 2024), ECF 44 https://storage.courtlistener.com/recap/gov.uscourts.cacd.947304/gov.uscourts.cacd.947304.44.0_1.pdf link

Memorandum of Points and Authorities in Support of Defendants' Motion to Dismiss Plaintiff's Amended Complaint, No. 2:24-cv-09566-MRA-AGR (C.D. Cal., 28 February 2025), ECF 50-1 https://storage.courtlistener.com/recap/gov.uscourts.cacd.947304/gov.uscourts.cacd.947304.50.1.pdf link