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 & citationAI Misidentifying Human Features: A Case Study - Importance of Safe and Secure AI
Read moreUncovering Bias in Chest X-Ray AI Classifiers: A Call for Responsible AI
Read moreThe Lekki Massacre: An Examination of Facebook's Content Labeling as 'False' in Light of Responsible AI
Read moreThousands Affected by Discriminatory AI Allegations: The Importance of Trustworthy AI
Read moreLawsuit Over Teacher Evaluation AI System in Houston Schools: A Case for Responsible AI Governance
Read moreMisinterpretation of Traffic Signals by Tesla's Autopilot Highlights Importance of Safe and Secure AI
Read moreNYPD's AI-Powered Canine Patrol Halted Amidst Public Concern: Ensuring Safe and Secure AI
Read moreRace Used as a Predictor in AI Models at Major Universities Raises Concerns over Fairness and Bias
Read moreFrench Welfare Services' 'Robo-Debt': A Case Study on Responsible AI and Harm Prevention
Read moreExamining Potential Bias in Twitter's Image Cropping Algorithm: A Responsible AI Perspective
Read moreControversy Over Tesla's Autopilot System: A Case Study in AI Safety
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.