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 & citationExamining LinkedIn's Alleged Gender Bias: A Case Study in AI Governance
Read moreResponsible AI in Action: Google Photos Ski Photo Incident
Read moreGoogle Translate misidentifies female historians and male nurses for European users: A look at the importance of responsible AI
Read moreApology from FaceApp Over Controversial Skin-Tone Altering Filter: A Case Study for Responsible AI
Read moreAI Bots on Wikipedia: From Collaboration to Conflict - Understanding the Need for Responsible AI Governance
Read moreLessons Learned from Kaggle's Fisheries Competition: The Role of Responsible AI in Harm Prevention
Read moreImproving AI's Performance in Writing Christmas Carols: A Step Towards Trustworthy AI
Read moreFaulty Reward Functions: An Exploration of Unintended Consequences in AI Systems
Read moreUnpatriotic Messages from Chinese Chatbots: An Incident Highlighting the Need for AI Governance
Read moreUber's Autonomous Vehicle Violates Traffic Regulations: Highlighting the Need for Safe and Secure AI
Read moreExploring the Yandex Chatbot Incident: A Case Study in 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.