Values Statement: We believe AI should cause no harm, but enhance the quality of human life, by proactively adopting our AI Governance framework.
Evidence-based Transparent For governance
AI Incidents
Data source & citationUK Passport Photo Checker Bias: Impact on Dark-Skinned Women and the Importance of Trustworthy AI
Read moreAI Incident Involving Jewish Baby Stroller Image – Understanding Its Impact on Responsible AI
Read moreYouTube's Radicalization Challenge: The Christchurch Shooting Incident and Responsible AI
Read moreApology for Exclusion of Front-Line Doctors in Stanford's COVID-19 Vaccine Plan: A Case Study on Safe and Secure AI
Read moreExploring Gender Bias Issues in Apple Card: A Case Study of Fintech AI Governance
Read moreFacebook Accused of Enabling Housing Discrimination by HUD - Highlighting the Need for Responsible AI Governance
Read moreCourt Ruling Highlights Discrimination Risks in AI-Based Rider Ranking Systems - A Call for Responsible AI
Read moreFacial Analysis Pause in Job Screening Services: A Step Towards Responsible AI
Read moreLawsuit over Teacher Evaluation System in Houston Schools Highlights Importance of Safe and Secure AI
Read moreMisinterpretation of Traffic Signals by Tesla's Autopilot: A Case Study in AI Incident Prevention
Read moreNYPD's AI-Powered Robot Dog Incident Highlights Importance of Safe and Secure AI
Read moreData source
Incident data is from the AI Incident Database (AIID).
When citing the database as a whole, please use:
McGregor, S. (2021) Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database. In Proceedings of the Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21). Virtual Conference.
Pre-print on arXiv · Database snapshots & citation guide
We use weekly snapshots of the AIID for stable reference. For the official suggested citation of a specific incident, use the “Cite this incident” link on each incident page.