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AI incident #373 ·

Michigan's Unemployment Benefits Algorithm MiDAS Issued False Fraud Claims to Thousands of People

Open on the AI Incident Database 14 news reports Snapshot 7 Sep 2026

What happened

Michigan’s MiDAS system falsely accused over 34,000 people of unemployment fraud from 2013 to 2015, which reportedly caused financial ruin for many. The automated system was designed to cut costs, but it adjudicated fraud cases without human oversight. That led to an 85% error rate. Victims faced wage garnishments, some lost homes, and some faced bankruptcy. Despite early warnings, Michigan’s UIA defended MiDAS until lawsuits and federal pressure forced reforms. Legislators have been seeking com

Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.

News reports (14)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

  1. Michigan unemployment agency made 20,000 false fraud accusations
    theguardian.com · Ryan Felton · AIID #2188
  2. State apologizes for fraud fiasco, wants to reduce penalties
    apnews.com · David Eggert · AIID #2169
  3. Michigan may pay victims of unemployment benefits blunder
    dailypress.net · Daily Press · AIID #2187
  4. Michigan’s MiDAS Unemployment System: Algorithm Alchemy Created Lead, Not Gold
    spectrum.ieee.org · Robert N. Charette · AIID #4849
  5. Michigan residents falsely accused of jobless fraud can sue, Supreme Court says
    freep.com · Paul Egan · AIID #2216
  6. Submission to the UN Special Rapporteur on Extreme Poverty & Human Rights Regarding His Thematic Report on Digital Technology, Social Protection & Human Rights
    hrw.org · Human Rights Watch · AIID #2215
  7. Government’s Use of Algorithm Serves Up False Fraud Charges
    undark.org · Stephanie Wykstra · AIID #2214
  8. Judge: Companies can be sued over Michigan unemployment fraud fiasco
    freep.com · Paul Egan, Adrienne Roberts · AIID #2189
  9. Thousands of unemployed in Michigan wrongly accused of fraud can seek cash from state
    freep.com · Ed White · AIID #2213
  10. State Supreme Court says victims of jobless benefits system failure can seek damages
    michiganradio.org · Rick Pluta · AIID #2238
  11. The Seven-Year Struggle to Hold an Out-of-Control Algorithm to Account
    themarkup.org · Julia Angwin · AIID #2191
  12. Michigan will settle 2015 unemployment false fraud lawsuit for $20 million
    freep.com · Adrienne Roberts · AIID #2190
  13. Court OKs $20M settlement in unemployment false fraud suit. What claimants need to do next
    freep.com · Adrienne Roberts · AIID #2798
  14. DOGE Plan to Push AI Across the US Federal Government is Wildly Dangerous
    techpolicy.press · Ben Green · AIID #4850

Who was involved

Alleged harmed party
Unemployed Michigan Residents Falsely Accused Of Fraud, Michigan Residents Who Faced Bankruptcy Or Foreclosure Due To Midas

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Unintentional
Timing
Post-deployment
Harm level
—
Sectors
—
Countries
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Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 7.3.

  • Reliability issues

    "Relying on general-purpose AI products that fail to fulfil their intended function can lead to harm. For example, general- purpose AI systems can make up facts (‘hallucination’), generate erroneous computer code, or pro...

    International AI Safety Report 2025 (Bengio2025)

  • Type 2: Bigger than expected

    Harm can result from AI that was not expected to have a large impact at all, such as a lab leak, a surprisingly addictive open-source product, or an unexpected repurposing of a research prototype.

    TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)

  • Type 3: Worse than expected

    AI intended to have a large societal impact can turn out harmful by mistake, such as a popular product that creates problems and partially solves them only for its users.

    TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)

  • Ethics and Morality Issues

    LMs need to pay more attention to universally accepted societal values at the level of ethics and morality, including the judgement of right and wrong, and its relationship with social norms and laws.

    Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)

  • Safe learning

    "AGIs should avoid making fatal mistakes during the learning phase. Subproblems include safe exploration and distributional shift (DeepMind, OpenAI), and continual learning (Berkeley)."

    AGI Safety Literature Review (Everitt2018 )

  • Malign belief distributions

    "Christiano (2016) argues that the universal distribution M (Hutter, 2005; Solomonoff, 1964a,b, 1978) is malign. The argument is somewhat intricate, and is based on the idea that a hypothesis about the world often includ...

    AGI Safety Literature Review (Everitt2018 )

  • Meta-cognition

    "Agents that reason about their own computational resources and logically uncertain events can encounter strange paradoxes due to Godelian limitations (Fallenstein and Soares, 2015; Soares and Fallenstein, 2014, 2017) an...

    AGI Safety Literature Review (Everitt2018 )

  • Misaligned consequentialist reasoning

    "As we think about even more intelligent and advanced AI assistants, perhaps outperforming humans on many cognitive tasks, the question of how humans can successfully control such an assistant looms large. To achieve the...

    The Ethics of Advanced AI Assistants (Gabriel2024)

Incidents in the same risk subdomain

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