Colleague's Face Unlocks iPhone X in China - Highlighting the Importance of Trustworthy AI
In a recent incident, a woman in China reported that her colleague was able to unlock her iPhone X using his face. This underscores the need...
Read moreEvidence-based Transparent For governance
In a recent incident, a woman in China reported that her colleague was able to unlock her iPhone X using his face. This underscores the need...
Read moreThis incident highlights the potential dangers of unauthorized use of smart assistants like Amazon's Alexa. The misuse of such technology ca...
Read moreThis investigation sheds light on possible techniques Amazon employs to guard against accidental Alexa activation during commercials. Unders...
Read moreDive into an intriguing case of an algorithm-induced dismissal, and learn how understanding AI decisions can lead to safer and trustworthy A...
Read moreAn unanticipated failure occurred when a Chinese AI-powered billboard failed to recognize the face it was intended to display, highlighting...
Read moreThis case study highlights an instance of a biased recruiting algorithm at Amazon, emphasizing the importance of trustworthy and unbiased AI...
Read moreThis self-driving Uber incident tragically demonstrates the importance of responsible AI governance. It maps to the Govern function in HISPI...
Read moreThe recent Elite: Dangerous AI incident highlighted an important lesson on responsible AI governance. When developers unintentionally enable...
Read moreAn intriguing incident involving the creation of a fake speech attributed to former U.S. President Barack Obama using artificial intelligenc...
Read moreExploring an instance where AI was used inappropriately in the criminal justice system, emphasizing the importance of responsible and safe A...
Read moreExploring an incident where MIT scientists inadvertently exposed an AI to violent content from Reddit, shedding light on the importance of r...
Read moreInvestigating the role of the National Residency Matching Program in shaping labor markets, this analysis highlights the importance of trust...
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.