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 & citationGoogle Photos AI Misfire: Recognition Flaws in Identifying Gorillas
Read moreCensorship of LGBTQ+ Content on Amazon Maps to the Govern Function in HISPI Project Cerebellum Trusted AI Model (TAIM): Preventing Harm and Ensuring Responsible AI
Read moreBias in Google's Sentiment Analysis API: A Reflection of Human Bias
Read moreGoogle's Comment-Ranking System: A Potential Risk for Responsible AI Governance
Read moreDebiasing Word Embeddings: Redefining AI Gender Stereotypes - Towards Trustworthy AI
Read moreProPublica Investigation Uncovers Machine Bias in Criminal Justice AI - A Case for Responsible AI Governance
Read moreUnpredictable AI Behavior Beyond Business Hours: A Case Study
Read moreExamining New York City Value-Added Data (Part 2): Leveraging AI for Trustworthy Data Analysis
Read more10 alarming cases highlighting the need for responsible AI governance: A look into AI incidents
Read moreAI Incident #178: Unintended Consequences in Autonomous Vehicle Decision-Making
Read moreYouTube Kids App Under Scrutiny for Serving Inappropriate Content - A Case Study on AI Incident Database
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.