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 & citationShutdown of 'Genderify': An AI Incident Highlighting the Importance of Responsible AI Governance
Read moreFalse 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 moreApology by Facebook for Misclassifying Video of Black Men as 'Primates': An AI Incident Highlighting the Need for Responsible AI
Read moreRevealed: Marketing Claims vs Real-world Performance of SF Gunshot Sensors - A Call for Trustworthy AI
Read moreAmazon's Algorithmic Decision-Making: A Case Study in AI Governance
Read moreExploring the Consequences of AI in Healthcare: Algorithmic Decisions and Patient Care
Read moreFacial Recognition Website: Ensuring Trustworthy AI and Preventing Misuse
Read moreExamining the Emergence of 'Robo-Debt' in French Welfare Services: A Case Study for Responsible AI
Read moreExploring Racial Bias in Automated Speech Recognition Systems – A Step Towards Responsible AI
Read moreThousands Misidentified by Biased AI: A Case for Responsible and Trustworthy AI
Read moreExamining Potential Bias in Twitter's Image Cropping Algorithm: A Call for Safe and Trustworthy 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.