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 & citationFalse Matches by Amazon's Face Recognition Highlight Need for Trustworthy AI - Amazon Case Study Maps to Govern Function in Project Cerebellum's Trusted AI Model (TAIM)
Read moreShutdown of 'Genderify': An AI Incident Highlighting the Importance of Responsible AI Governance
Read moreMisleading AI Decisions in Amazon's Cameras: A Harm Prevention Issue
Read moreExamining Allegations of Bias in TikTok's Algorithm: A Look at Responsible AI Governance
Read moreExploring Bias in AI: The Case of Islamophobia
Read moreControversial Use of AI in Mass Layoffs: Xsolla Incident
Read moreAI-controlled Mall Robot Incident: Harm Prevention and Safe AI in Focus
Read moreThe Impact of Brand Safety Technology on News Defunding - Understanding Responsible AI Governance
Read moreExploring the Impact: The Case of an Admissions AI Algorithm
Read moreIsraeli Farmer's Dispute with AI Algorithm Highlights Importance of Trustworthy AI
Read moreAccount Deletion of a Latina Trans Women on TikTok: An Incident Highlighting the Need for Responsible 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.