Domains
Domain Atlas
“How does this show up in specific domains?”
Social services are a high-stakes frontier for AI governance: predictive scores and generative assistants already shape who gets investigated, helped, paid, and believed. 6 domains, 30 use cases, and 79 documented case files, every factual claim cited to the Evidence Registry.
Domain Atlas
The Six Domains
Child welfare & family services
14 case filesPredictive screening and profiling where the cost of both false alarms and misses lands on families — and where the human override layer has measurably mattered.
Public benefits & eligibility
21 case filesFraud scoring, eligibility automation, and care allocation — the domain with the largest documented harms, almost all of them ending in courts and commissions.
Caseworker documentation & copilots
11 case filesGenerative and rules-based assistants that transcribe, summarize, triage, and increasingly draft the records and decisions institutions run on — meeting scribes, mail-triage filters, evidence summarizers, ruling drafters, and automation that removes the caseworker from the routine path entirely. The human review step is the load-bearing control, and the throughput that justifies the tool is the same pressure that erodes it — with a verifier that checks human work at one edge of the range and deployments halted after the harm was measured at the other.
Benefits navigation & public-facing chat
11 case filesConversational systems standing between the public and their benefits — public-facing chatbots, adviser-gated copilots, federated whole-of-government fleets, and navigation intermediaries whose quiet removal is itself the harm. An authoritative wrong answer is indistinguishable, to its victim, from policy; what sets the exposure is whether a professional gates the answer, whether anyone measures accuracy at all rather than mere deflection, and whether the whole channel rests on a single actor who can switch it off. The Lab networks in this domain model only what happens inside the operating organization — its operators, engines, and knowledge stores; the members of the public asking the questions sit outside the dynamics, and harm to them is documented in each case file, never computed on a diagram.
Housing & homelessness services
11 case filesPrioritization and prevention scores deciding who reaches scarce housing help first — where a more accurate model can still leave the same people under-served, and the quietest harm is often the person the system never surfaced.
Behavioral-health & crisis triage
11 case filesRisk scores and triage rankers deciding whose crisis is seen first — where the rare event is nearly impossible to predict reliably, the flag moves a proxy more surely than the outcome, and a score can quietly gate access to care.
Pressures
Institutional pressures
The recurring forces that bend deployed systems away from their evaluated behavior. Each domain page names the pressures that dominate it. This vocabulary is conceptual framing, drawn from the documented cases.
Caseload surge
Demand outruns staffing; per-case attention shrinks and review becomes triage.
Reviewer bottleneck
One fixed-capacity checking stage sits between AI output and consequence; everything queues behind it.
Austerity & recovery incentives
Cost-cutting and overpayment-recovery targets tilt the system toward denial and enforcement errors.
Vendor opacity
The deploying institution cannot inspect the model, data, or update pipeline it is accountable for.
Deadline pressure
Statutory or managerial timeliness rules reward fast approval of machine output over slow disagreement.
Staff turnover
Experienced skepticism leaves; new staff calibrate their trust on the tool itself.
Data & policy drift
The world, the intake process, and the rules change under a system trained on how things used to be.
Compliance over substance
Paper controls (sign-offs, checklists) satisfy audits while the behavior they describe erodes.
Want to see these pressures act on a system? Stress-test them in the PAN Lab →
These case files are documented after the harm. Mapping a live deployment's pathways and pressures before the incident report is engagement work.
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