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 Algorithm Analyzing X-Rays Appears to Predict Patient's Race Inaccurately
Read moreExploring Exaggerated AI Claims in Healthcare: A Cautionary Tale
Read moreSettlement of $550 million in Facebook's Privacy Lawsuit Over Facial Recognition Technology: A Case for Responsible AI
Read moreExamining Allegations of Potential Military Use of Autonomous Aerial Vehicles: The Case of Libya
Read moreUncontrolled AI Experimentation: A Reddit GPT-3 Bot Case Study – Highlighting the Importance of Responsible AI Governance
Read moreControversial Downsizing at Xsolla: Big Data and AI Analysis Lead to Employee Terminations
Read moreAddressing Bias in AI: The Case of Islamophobia
Read moreFacial Recognition Website Highlights Need for Responsible AI Governance
Read moreExploring Algorithmic Decision-Making in Amazon's Flex Workforce: A Case for Responsible AI Governance
Read moreUnderstanding the Impact of AI on Healthcare: An Examination of an AI Incident
Read moreCourtroom Testimony Unveils Potential Misrepresentation of San Francisco Gunshot Sensor Accuracy - Raising Questions about 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.