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Alleged Macy's Facial Recognition Error Leads to Wrongful Arrest and Subsequent Sexual Assault in Jail

3 news reports Synced from source · record last edited 4 Sep 2026

In brief

An AI system built by Facial recognition system developers and deployed by Macy's and Facial recognition system deployers allegedly harmed Macy's customers, Harvey Murphy Jr and 3 others.

Risk domain
AI system safety, failures, and limitations Lack of capability or robustness
Occurred
Coverage
3 reportsJan 2024

What happened

Harvey Murphy Jr. was wrongfully accused of robbing a Sunglass Hut due to an alleged misidentification by the facial recognition system operated by Macy's. While in custody for ten days, he was sexually assaulted. He is now suing Macy's, EssilorLuxottica (Sunglass Hut's parent), and others, for $10 million.

Laws that address this harm

Policy angle: Classified under AI system safety, failures, and limitations (Lack of capability or robustness) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case.

Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).

News reports (3)

Titles link to the original publisher; report text is not reproduced here.

  1. Man sues Macy’s, saying false facial recognition match led to jail assault
    washingtonpost.com · Drew Harwell
  2. Facial recognition used after Sunglass Hut robbery led to man’s wrongful jailing, says suit
    theguardian.com · Johana Bhuiyan
  3. Man Jailed, Raped, and Beaten After False Facial Recognition Match, $10M Lawsuit Alleges
    vice.com · Matthew Gault

Who was involved

Alleged harmed party
Macy's customers Harvey Murphy Jr General public of the United States General public Consumers

AI systems implicated

Facial recognition systemsAI-enabled decision support systems

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Unintentional
Timing
Post-deployment
Harm level
—
Sectors
—
Countries
—

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 )

  • Technical and operational risks

    "To date, technical limitations and vulnerabilities are present in most generative AI models in various contexts. Consequently, malicious users find it easier to breach an AI system’s safety and ethical guardrails to e...

    Regulating under Uncertainty: Governance Options for Generative AI (G'sell2024)

Linked by editors or by text similarity in the source dataset.

Incidents in the same risk subdomain

All incidents in this subdomain

Source record: incident #630 on the AI Incident Database · all 3 reports