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AI incident #598 ·

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

Open on the AI Incident Database 1 news report Synced from the AIID API · record last edited 4 Sep 2026

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.

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 (1)

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

  1. JPSO used facial recognition technology to arrest a man. The tech was wrong.
    nola.com · John Simerman · AIID #3274

Who was involved

Alleged harmed party
Randal Reid Minority groups Black people
On AIID: 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)

  • Fairness

    Avoiding bias and ensuring no disparate performance

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

  • 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)

  • 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)

  • Erasing social groups

    people, attributes, or artifacts associated with specific social groups are systematically absent or under-represented... Design choices [143] and training data [212] influence which people and experiences are legible to...

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

Linked by AIID editors or by its text-similarity model.

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