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False Arrest of Georgia Man Due to Louisiana Police's Faulty Facial Recognition Technology

1 news report Synced from source · record last edited 4 Sep 2026

In brief

An AI system built by Unknown and Facial recognition system developers and deployed by Law enforcement, Jefferson Parish Sheriff’s Office and 1 other allegedly harmed Randal Reid, Minority groups and 1 other.

Risk domain
Discrimination and Toxicity Unequal performance across groups
Occurred
Coverage
1 reportJan 2023

What happened

The Jefferson Parish Sheriff’s Office in Louisiana relied on facial recognition technology to identify suspects for the alleged theft of luxury purses, resulting in a man in Georgia, Randal Reid, being arrested. However, the technology produced a false match, leading to Reid's arrest and subsequent release. This incident highlights the potential pitfalls of facial recognition technology in law enforcement.

Laws that address this harm

Policy angle: Classified under Discrimination and Toxicity (Unequal performance across groups) 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 (1)

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

  1. JPSO used facial recognition technology to arrest a man. The tech was wrong.
    nola.com · John Simerman

Who was involved

Alleged harmed party
Randal Reid Minority groups Black people

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 1.3.

  • Bias and discrimination (value embedding)

    "Generative AI models may also be subject to the “value embedding” phenomenon.361 “Value embedding” refers to the fact that developers of generative AI models strive to minimize biased outputs by retraining their models...

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

  • Impact on affected communities

    "It is important to include the perspectives or concerns of communities that are affected by model outcomes when designing and building models. Failing to include these perspectives makes it difficult to understand the r...

    AI Risk Atlas (IBM2025)

  • Unfair capability distribution

    "Performing worse for some groups than others in a way that harms the worse-off group"

    A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

  • Disparate Performance

    The LLM’s performances can differ significantly across different groups of users. For example, the question-answering capability showed significant performance differences across different racial and social status groups...

    Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

  • Fairness

    Avoiding bias and ensuring no disparate performance

    Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

  • Ideological Homogenization from Value Embedding

    "The increasing integration of general purpose AI models into every-day life raises concerns around their embedded normative values. The reach of a small number of AI models to a large number of people around the world c...

    Governing General Purpose AI: A Comprehensive Map of Unreliability, Misuse and Systemic Risks (Maham2023 )

  • Fairness

    This challenge appears when the learning model leads to a decision that is biased to some sensitive attributes... data itself could be biased, which results in unfair decisions. Therefore, this problem should be solved o...

    A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions (Saghiri2022)

  • Increased labor

    increased burden (e.g., time spent) or effort required by members of certain social groups to make systems or products work as well for them as others

    Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (Shelby2023)

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

Incidents in the same risk subdomain

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Other incidents involving Law enforcement

Source record: incident #598 on the AI Incident Database · all 1 report