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 & citationRace Used as a High-Impact Predictor in AI Models at Major Universities: A Case for Responsible AI Governance
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Read moreThousands Wrongly Accused by Biased AI Algorithm: A Case Study in Discriminatory AI
Read moreStudy Highlights Challenges for Personal Voice Assistants in Recognizing Black Voices: A Step Towards Responsible AI
Read moreBias in Twitter's Photo Cropping Algorithm: Focus on White Females
Read moreExploring the Potential Impact of California's Equity-Focused Algorithm on Vaccine Distribution: An Important Step Towards Trustworthy AI
Read moreContentious Claims over Tesla's Autopilot Safety: A Case Study for Responsible AI
Read moreSouth Korean Chatbot Incident Highlights the Importance of Responsible Data Handling in AI
Read moreAI-Powered Patent Exposure: Huawei's Uighur Detection System under Scrutiny - A Case for Trustworthy AI
Read moreAI Misidentification at Roller Rink: A Case for Responsible AI Harm Prevention
Read moreFacial Recognition Website: Potential for Law Enforcement Misuse or Privacy Invasion - Highlighting the Need for 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.