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 & citationDiscriminatory AI Allegedly Falsely Accuses Thousands: A Case for Safe and Secure AI
Read moreFrench Welfare Services' 'Robo-Debt' Incident: An Examination of Responsible AI and Harm Prevention
Read moreRace Used as Predictor in AI Models for Student Success: A Case Study on Major Universities
Read moreFacebook Charged for Alleged Discrimination in Housing via AI Practices - Understanding the Govern Function of Project Cerebellum
Read moreBias in AI Systems: The Case of the UK Passport Photo Checker - A Call for Safe and Trustworthy AI
Read moreAI Bias Incident: Jewish Baby Stroller Misclassification – Ensuring Trustworthy AI
Read moreYouTube's Radicalization Trap: The Role in Preventing Harm from AI-Fueled Violence - A Case Study
Read moreStanford Hospital's Vaccine Distribution Sparks Protest by Frontline Workers: A Case Study in AI Governance
Read moreGender Bias Allegations Against Goldman Sachs in Apple Card Algorithm - Highlighting the Need for Trustworthy AI
Read moreFacial Analysis in Job Screening Services Paused Amidst Controversy - A Case Study on Safe AI Implementation
Read moreMisinterpretation of Traffic Signals by Tesla's Autopilot: A Case Study for Responsible 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.