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 & citationExamining Amazon's Automated Employment Decisions: A Case Study in AI Governance
Read moreUnveiling the Truth: SF Gunshot Sensors' Accuracy as a 'Marketing Gimmick', Raising Concerns for Responsible AI
Read moreFacebook AI Misclassifies Video of Black Men as Primates: Harm Prevention Issue Highlighted
Read moreMisidentification of U.S. Congress Members by Amazon's Facial Recognition Technology: A Harmful AI Incident
Read moreClosure of 'Genderify' AI Service Highlights Importance of Responsible AI Governance
Read moreMisidentifications by Amazon's AI Cameras: Harm Prevention in Trustworthy AI
Read moreExamining Allegations of Bias in TikTok's Recommendation System: Ensuring Safe and Trustworthy AI
Read moreAddressing Bias in AI: The Case of Islamophobia - Harm Prevention through Responsible AI
Read moreControversial Employee Dismissals by Xsolla: A Case Study on AI Accountability
Read moreUnregulated AI Use: A Case Study - GPT-3 Bot Incident on Reddit
Read morePotential Use of Autonomous Weapons in Libya: A Look into AI Governance
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.