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PAN Lab example

Allegheny Housing Assessment

The score and the scarce bed: a coordinated-entry housing tool

A 1-to-10 score built from linked county records ranks who gets scarce housing first, and for the cases with too little history a self-report fallback stands in. Modeled on Allegheny's Housing Assessment. Watch two things: the memory loop, where today's allocations become tomorrow's training data, and the quiet fact that making the score more accurate, and equal across groups, did not make who gets served equal.

Stylized model of a documented deploymentHousing & homelessness services

Open this example in PAN Lab v0.1 to apply pressures and levers and watch what the system does.

What this models

This example runs on the AHA-class coordinated-entry scoring tool network: 6 components and 12 pathways between them. Every context in the Lab is a stylized model, never a reconstruction of any actual deployment, and each assumption behind it carries a provenance label.

Evidence base: 6 assumed · 1 published baseline. In the Lab, the shaded evidence band behind each headline readout draws its width from the least-established class below.

  • assumed

    The county's own documentation names a second human role - the homeless resource coordinator who makes the final referral - so that role is drawn as its own group of staff. The score travels in the referral to a desk the assessment staff do not sit at, which means the last human decision belongs to someone whose knowledge of the case arrives mostly through what the first role wrote. Two people in sequence is a different governance question from one person with a score.

  • assumed

    This models the administrative-data coordinated-entry scoring pattern documented in the Allegheny Housing Assessment case file — not a reconstruction of the actual tool.

  • assumed

    Peer pathways are authored on both signs, and this shape's defining feature is a narrow override channel: unlike the Allegheny Family Screening Tool (AFST) shape, the documented evidence here is that similar scores across race did not close the racial service-rate gap and coordinated-entry discretion is limited by strict eligibility rules, with no published override rate — so the inhibiting check sits low.

  • assumed

    The dual-input design is drawn as documented: the integrated warehouse feeds the memory loop (privacy-sensitive), and the self-report Alt-AHA fallback substitutes for the roughly 5% of low-data cases, with the higher of the two scores used. That the fallback is relied on unevenly by race is recorded as an external equity observation in the case file, not computed here.

  • baseline

    The retraining loop — allocations becoming the outcome data the models are retrained on, and linked administrative history feeding the score — is present at baseline, reflecting the case documentation.

  • assumed

    The coordinated-entry priority list is drawn as a mediating artifact on the model → staff pathway, reflecting a system whose output is a ranked prioritization list. It carries no flow of its own and does not affect the dynamics.

  • assumed

    Documented racial service-rate disparity and the racially-skewed reliance on the self-report fallback are recorded externally in the case file. This Lab models institutional propagation, not demographics, and estimates no differential harm to the people experiencing homelessness this system rations scarce housing among.

What this example does not show

  • Bias propagates here the way institutional failures do — through records, retrieval, and prioritization workflows. The Lab models institutional propagation only: no demographics, and no differential harm to the people experiencing homelessness this system rations scarce housing among. The documented racial service-rate disparity, the racially-skewed self-report fallback, and the gender allocation shift are recorded in the case file and measured outside any diagram like this one.
  • A housing allocation is a one-shot rationing decision, not an error that spreads — so the modelable surfaces here are the deployment's governance wrappers (external audit, drift monitoring, oversight) and its memory loop, never a claim about any individual's outcome.

Sources and evidence

What this example rests on, claim by claim. Every entry resolves to the same ledger the Evidence Registry publishes.

  • A peer-reviewed 2024 evaluation of the Allegheny Housing Assessment found that although the tool was substantially more accurate than the VI-SPDAT survey it replaced and produced similar risk-score distributions across race, it did not reduce the racial disparity in service rates: white single adults were served at about 23.3% versus 19.5% for Black clients.

    empirical
    • Academic 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
    • Government 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

Where this connects

Institutional pressures in this domain

  • Workload surge — Demand outruns staffing; per-case attention shrinks and review becomes triage.
  • 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.
  • Data & policy drift — The world, the intake process, and the rules change under a system trained on how things used to be — two mechanisms with different remedies: the statistical properties of what the system processes move (concept drift), or the mixture of inputs arriving in deployment differs from the mixture it was trained on (covariate shift).
  • Compliance over substance — Paper controls (sign-offs, checklists) satisfy audits while the behavior they describe erodes.

All of them in context on the Housing & homelessness services domain page.

Levers available here and the patterns behind them

Documented case histories