Alleged Misuse of Facial Recognition Technology by Law Enforcement Reportedly Leading to Wrongful Arrests and Violations of Investigative Standards
Description
Law enforcement agencies across the U.S. have allegedly been misusing AI-powered facial recognition technology, leading to wrongful arrests and significant harm to at least eight individuals. Officers have reportedly been bypassing investigative standards, relying on uncorroborated AI matches to build cases, allegedly resulting in prolonged detentions, reputational damage, and personal trauma.
Actors
Alleged Deployers
- florence-kentucky-police-department
- evansville-indiana-police-department
- detroit-police-department
- coral-springs-florida-police-department
- bradenton-florida-police-department
- austin-police-department
Alleged Developers
- facial-recognition-system-developers
- developers-of-mugshot-recognition-software
- clearview-ai
Harmed Parties
- wrongfully-arrested-individuals
- vulnerable-communities
- robert-williams
- quran-reid
- porcha-woodruff
- people-of-color
- nijeer-parks
- jason-vernau
- christopher-gatlin
- black-people
- alonzo-sawyer
Classification
Risk Domain
AI Safety & FailuresRisk Subdomain
7.3. Lack of capability or robustness
MIT Risk Profile
Topics
Source
Related Incidents (AI Safety & Failures)
View all →Research Papers on This Topic
View all →Concrete Problems in AI Safety
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI
Representation Engineering: A Top-Down Approach to AI Transparency
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
Mitigation Tools
Tools related to this risk domain, via shared responsible AI topics.