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PAN Lab example

Michigan MiDAS

Automation without review: a benefits-fraud system

A fraud system writes determinations straight into people's benefit records with no human in between — then enforcement copies them automatically. Modeled on Michigan's MiDAS. The damage flows through the records, not the staff, so operator-side levers barely reach it.

Stylized model of a documented deploymentPublic benefits & eligibility

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 MiDAS-class automated determination system network: 4 components and 8 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: 3 assumed · 2 published baseline. In the Lab, the shaded evidence band behind each headline readout draws its width from the least-established class below.

  • assumed

    This models the automated-determination pattern documented in the Michigan MiDAS case file — not a reconstruction of the actual system.

  • baseline

    The model-to-model self-loop encodes correlated error: the documented failure was one automated logic applied uniformly, so its mistakes repeated at caseload scale rather than averaging out.

  • baseline

    Human review capacity is effectively absent at baseline, matching the case documentation's description of the automation posture.

  • assumed

    Downstream enforcement replicates the record without an independent check.

  • assumed

    The record-side reconciliation check is drawn on the map but runs dry at baseline: replicated flags were actioned downstream with no independent audit against source — the record-side analog of the missing peer and model checks, and the gap the replication-reconciliation lever closes.

Sources and evidence

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

  • A single automated rule set applied uniformly and without human review produced tens of thousands of correlated wrongful fraud determinations in the documented Michigan MiDAS case — one flaw repeating at caseload scale rather than averaging out.

    empirical
    • Government Michigan AG, settlement of civil-rights class action (Bauserman, 2022) https://www.michigan.gov/ag/news/press-releases/2022/10/20/som-settlement-of-civil-rights-class-action-alleging-false-accusations-of-unemployment-fraud
    • Investigative 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

  • In the documented MiDAS case, error among no-review auto-adjudications ran roughly 93%, and determinations erred at about 85% without human review versus 44% with it.

    empirical
    • Government Michigan AG, settlement of civil-rights class action (Bauserman, 2022) https://www.michigan.gov/ag/news/press-releases/2022/10/20/som-settlement-of-civil-rights-class-action-alleging-false-accusations-of-unemployment-fraud
    • Investigative 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
    • Investigative AI Incident Database, Incident 373 (MiDAS false fraud claims) https://incidentdatabase.ai/cite/373/
    • Advocacy Benefits Tech Advocacy Hub, Michigan UI False Fraud Determinations https://www.btah.org/case-study/michigan-unemployment-insurance-false-fraud-determinations.html

  • Documented benefit-automation failures replicated determinations into downstream systems with no independent reconciliation against the source records — Michigan MiDAS actioned replicated flags and Robodebt reversed the onus onto recipients.

    empirical
    • Government Michigan AG, settlement of civil-rights class action (Bauserman, 2022) https://www.michigan.gov/ag/news/press-releases/2022/10/20/som-settlement-of-civil-rights-class-action-alleging-false-accusations-of-unemployment-fraud
    • Investigative 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
    • Government Royal Commission into the Robodebt Scheme, Report (2023) https://robodebt.royalcommission.gov.au/publications/report
    • Investigative Law Society Journal, Crude, cruel and unlawful: Robodebt findings https://lsj.com.au/articles/crude-cruel-and-unlawful-robodebt-royal-commission-findings/

Where this connects

Institutional pressures in this domain

  • 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.
  • Compliance over substance — Paper controls (sign-offs, checklists) satisfy audits while the behavior they describe erodes.
  • Reviewer bottleneck — One fixed-capacity checking stage sits between AI output and consequence; everything queues behind it.

All of them in context on the Public benefits & eligibility domain page.

Levers available here and the patterns behind them

Documented case histories