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Domain Atlas

Child welfare & family services

Predictive screening and profiling where the cost of both false alarms and misses lands on families — and where the human override layer has measurably mattered.

What AI is doing here

Maltreatment call screening

Predictive

Risk scores supporting screen-in/screen-out decisions on child-maltreatment referrals.

Family risk prediction

Predictive

Longitudinal risk models over family and administrative data to prioritize investigation or services.

Early-help profiling

Predictive

Mining council/agency data to flag families for preventive outreach before crisis.

Case notes as training data

Predictive

Using narrative case records to train predictive models — importing the biases and errors those records contain.

What has gone wrong — and right

Documented deployments, presented as stylized model organizations with full citations.

Allegheny Family Screening Tool

Allegheny County, Pennsylvania, USA

The most-studied predictive risk score in child welfare — and evidence that the human override layer is where equity was won or lost.

Illinois Rapid Safety Feedback

Illinois, USA

A child-welfare risk tool that flagged thousands of children at extreme risk while missing actual fatalities — discontinued in 2017.

Oregon Safety at Screening

Oregon, USA

An AFST-derived screening tool that Oregon shelved in 2022 amid equity concerns — a rare pre-crisis discontinuation.

Hackney / Xantura Early Help Profiling

London Borough of Hackney, UK

A council's family-profiling pilot quietly ended after data-quality and effectiveness problems — small-scale, instructive failure.

Who is in the system

  • Frontline workers. Caseworkers, screeners, eligibility staff — the operator network whose judgment the system augments or erodes.
  • Supervisors & QA. The institutional correction layer: overrides, second reads, quality review.
  • Agency leadership. Owns procurement, policy, and the authority map; answers for the system publicly.
  • Served people & families. Those the decisions land on. Deliberately outside the PAN dynamics — their outcomes are measured, never simulated.
  • Regulators & oversight bodies. Boards, auditors, data-protection officers, inspectorates — external correction capacity.
  • Advocates & community organizations. Surface harms institutions do not see; historically the earliest accurate signal.

Dominant pressures

  • Caseload surge. Demand outruns staffing; per-case attention shrinks and review becomes triage.
  • 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.

Questions leaders should be asking

  1. 1. What does a risk score change about a worker's next action — and is that mapping written down anywhere?
  2. 2. Are overrides tracked, and does anyone know whether they are improving or degrading equity?
  3. 3. If the tool were saturating workers with alerts, how would leadership find out?
  4. 4. What would trigger discontinuation, and who holds the authority to trigger it?

For the actions behind these questions, see the Practice Library.