Misidentified by AI: Addressing Unfair Bias in Algorithms
Exploring a case of algorithmic misidentification, this article highlights the importance of responsible AI governance and trustworthy AI. T...
Read moreValues Statement: We believe AI should cause no harm, but enhance the quality of human life, by proactively adopting our AI Governance framework.
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AI Incidents
Data source & citationLawsuit Filed Over Alleged Bias in Google Instant Search Results: A Case for Responsible AI Governance
This recent lawsuit in France highlights the need for safe, secure, and trustworthy AI. The case underscores the importance of responsible A...
Read moreUnchecked Use of Live Facial Recognition Puts Children at Risk - Mapping to the Govern Function in HISPI Project Cerebellum Trusted AI Model
Exploring a troubling instance of live facial recognition misuse, we highlight potential risks for children falsely identified as criminals....
Read moreMalfunctioning Police Robot Sings Instead of Assisting in Crime Report - Highlighting the Need for Responsible AI
An unexpected incident involving a police robot occurred when it was unable to assist a woman reporting a crime, instead opting to sing a so...
Read moreExploring the Hidden AI Model Regulating University Admissions
Delve into this AI incident, which demonstrates the role of responsible AI in governance. This algorithm is shaping college enrollment decis...
Read moreAI System in Kidney Transplant Matching Causing Disparities Against Black Patients - A Case for Responsible AI Governance
This alarming incident showcases the critical need for trustworthy, safe, and secure AI systems. The AI algorithm used in kidney transplant...
Read moreHilarious AI Misinterpretation: Football vs Ref's Bald Head - Emphasizing the Need for Safe & Secure AI
Experience a lighthearted yet crucial lesson in AI's potential pitfalls when an AI system misidentified a referee's bald head as a football....
Read moreExploring Bias in Chest X-Ray AI Classifiers: A Step Towards Trustworthy AI
This investigation uncovers racial, gender, and socioeconomic bias in chest X-ray classifiers, highlighting the need for safe and secure AI....
Read moreLekki Massacre: Analyzing Facebook's Labeling of Content as 'False' - A Case Study for Responsible AI
In this article, we delve into the incident regarding the Lekki Massacre in October 2020 and Facebook's decision to label related content as...
Read moreUnderstanding Controversies in Spam Filters: An Examination of AI Governance
Explore the surprising controversies behind spam filters, a seemingly mundane application of AI. This AI incident maps to the Govern functio...
Read moreFacebook's Struggle to Combat Misinformation: COVID-19 and Voting False Claims
This incident underscores the challenges in maintaining safe and secure AI, particularly in content moderation systems. The failure of Faceb...
Read moreAI Incident: Robot Fears Unfounded? AI Warns It May Not Avoid Destroying Humanity
This AI incident underscores the importance of responsible AI governance. The incident maps to the Govern function in HISPI Project Cerebell...
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.