Detroit Police Wrongfully Arrested Black Man Due To Faulty Facial Recognition

Laws that address this harm

Policy angle: Classified under Discrimination and Toxicity (Unfair discrimination and misrepresentation) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case.

• Colorado AI Act Colorado (United States) · Repealed · binding
• India DPDP Act India · Partially applicable · binding
• Law No. 132/2025 on artificial intelligence Italy · In force · binding
• NYC Local Law 144 (automated employment decision tools) New York (United States) · In force · binding
• EU AI Act European Union · Partially applicable · binding

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. 10 Jul 2020
   Controversial Detroit facial recognition got him arrested for a crime he didn’t commit

   freep.com · Elisha Anderson
2. 4 Sep 2020
   Faulty Facial Recognition Led to His Arrest—Now He’s Suing

   vice.com · Natalie O'Neill
3. 19 Nov 2020
   Wrongful arrest exposes racial bias in facial recognition technology

   cbsnews.com · Elaisha Stokes

Who was involved

Alleged deployer

Detroit Police Department

Alleged developer

DataWorks Plus

Alleged harmed party

Michael Oliver Black people in Detroit Black people

AI systems implicated

Facial recognition systemsAI-enabled decision support systems

Classification (MIT AI Risk Repository taxonomy)

Risk domain

Discrimination and Toxicity

Risk subdomain

1.1 Unfair discrimination and misrepresentation

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 1.1.

• Discrimination

  "Discrimination - Unfair or inadequate treatment or arbitrary distinction based on a person’s race, ethnicity, age, gender, sexual preference, religion, national origin, marital status, disability, language, or other pro...

  A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

• Harms of Representation and Other Biases

  "A pretrained LLM generally has many of the stereotypical biases commonly present in the human society (Touvron et al., 2023). This makes it difficult for users to trust that LLMs will work well for them and not produce...

  Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

• Risks from bias and underrepresentation

  "The outputs and impacts of general- purpose AI systems can be biased with respect to various aspects of human identity, including race, gender, culture, age, and disability. This creates risks in high- stakes domains su...

  International Scientific Report on the Safety of Advanced AI (Bengio2024)

• Bias

  "General-purpose AI systems can amplify social and political biases, causing concrete harm. They frequently display biases with respect to race, gender, culture, age, disability, political opinion, or other aspects of hu...

  International AI Safety Report 2025 (Bengio2025)

• Biased Training Data

  "Compared with the definition of toxicity, the definition of bias is more subjective and contextdependent. Based on previous work [97], [101], we describe the bias as disparities that could raise demographic differences...

  Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

• Toxicity and Bias Tendencies

  "Extensive data collection in LLMs brings toxic content and stereotypical bias into the training data."

  Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

• Bias

  "The training datasets of LLMs may contain biased information that leads LLMs to generate outputs with social biases"

  Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

• Broken systems

  "These are the most mentioned cases. They refer to situations where the algorithm or the training data lead to unreliable outputs. These systems frequently assign disproportionate weight to some variables, like race or g...

  Navigating the Landscape of AI Ethics and Responsibility (Cunha2023)

Incidents in the same risk subdomain

• 2 Apr 2025DOGE Reportedly Relied on Unvetted ChatGPT Outputs in Canceling National Endowment for the Humanities Grants
• 23 Mar 2025Sora Video Generator Has Reportedly Been Creating Biased Human Representations Across Race, Gender, and Disability
• 3 Jan 2025Meta AI Characters Allegedly Exhibited Racism, Fabricated Identities, and Exploited User Trust
• 15 Oct 2024Algorithmic Bias in French Welfare System Allegedly Discriminates Against Marginalized Groups
• 15 Oct 2024Alleged AI-Generated Photo Alteration Leads to Inappropriate Modifications in Speaker's Conference Picture
• 10 Jul 2024Department for Work and Pensions (DWP) AI Systems Allegedly Discriminate Against Single Mothers

Other incidents involving Detroit Police Department

• 13 Jan 2025Alleged Misuse of Facial Recognition Technology by Law Enforcement Reportedly Leading to Wrongful Arrests and Violations of Investigative Standards
• 16 Feb 2023Facial Recognition Misidentifies Pregnant Woman Leading to False Arrest in Detroit
• 30 Jan 2020Detroit Police Allegedly Wrongfully Arrested Black Man Due to Purportedly Faulty Facial Recognition Technology