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 & citationExploring Bias in TikTok's Algorithm: A Discussion on Responsible AI
Read moreUnfair AI Penalties: Amazon's AI Cameras Mistaking Human Errors
Read moreAddressing AI Bias: The Case of 'Genderify' and the Importance of Trustworthy AI
Read moreFalse Matches by Amazon's Facial Recognition: A Pivotal AI Incident for Responsible Governance
Read moreFacebook Apologies for Mislabeling Video of Black Men as Primates: An AI Incident Mapping to the Govern Function in HISPI Project Cerebellum Trusted AI Model (TAIM)
Read moreFootball referee mistakenly identified by AI as a ball: An incident highlighting the need for safe and secure AI
Read moreFacebook Labels Content Related to Lekki Massacre (October 20) Incident as False: Ensuring Trustworthy AI
Read moreUncovering Bias in Chest X-Ray Classifiers: A Call for Responsible AI
Read moreExploring Controversies in Spam Filter AI: A Look Beyond Efficiency
Read moreAlgorithm Bias Cases: Discrimination Against Black Patients in Kidney Transplant Allocation
Read moreExploring the Unseen AI Decision-Maker Impacting University Admissions: A Discussion on 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.