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 & citationHow a Discriminatory Algorithm Wrongly Accused Thousands of Families of Fraud
Read moreRacial disparities in automated speech recognition
Read moreWhy Twitter’s image cropping algorithm appears to have white bias
Read moreCalifornia's “Equity” Algorithm Could Leave 2 Million Struggling Californians Without Additional Vaccine Supply
Read moreTesla Says Autopilot Makes Its Cars Safer. Crash Victims Say It Kills.
Read moreA South Korean Chatbot Shows Just How Sloppy Tech Companies Can Be With User Data
Read moreAI-Powered Patent Exposure: Huawei's Controversial Technology for Uighur Detection
This AI incident reveals a concerning patent application by Huawei, aimed at developing AI technology to detect Uighurs. It underscores the...
Read moreStruggles in Facial Recognition: A Case Study on Algorithms Identifying Black Faces
This case study highlights the challenges faced by the best algorithms in recognizing black faces, emphasizing the importance of responsible...
Read moreThis facial recognition website can turn anyone into a cop — or a stalker
Read moreWhat Happens When An Algorithm Cuts Your Health Care
Read moreAmazon is using algorithms with little human intervention to fire Flex workers
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.