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

Autopilot Safety Debate: Tesla Claims Improved Safety, While Some Warn of Risks

Incident involving Tesla Autopilot raises questions about AI safety in self-driving cars. This AI incident maps to the Govern function in HI...

The Importance of Responsible AI: A Case Study on a South Korean Chatbot and User Data Privacy

This AI incident, involving a chatbot in South Korea, highlights the need for trustworthy AI. The mishandling of user data underscores the s...

Disturbing Patent Exposed: Huawei's AI-Powered Uighur Detection Raises Concerns for Responsible AI

This AI incident raises concerns about the ethical use of artificial intelligence. The patent, belonging to Huawei, suggests development of...

Facial Recognition Error: Black Teen Misidentified by AI System at Skating Rink - Harm Prevention in Responsible AI

An unfortunate incident occurred where a facial recognition system incorrectly identified a black teen at a skating rink, leading to her rem...

Facial Recognition Website Highlights Need for Responsible AI Governance

This facial recognition website, which allows anyone to perform searches based on a person's image, raises concerns about privacy and potent...

Exploring the Consequences of Algorithms in Healthcare: A Case Study

Investigate an instance where algorithmic decision-making impacted healthcare services. This AI incident maps to the Govern function in HISP...

Algorithms Used by Amazon for Firing Flex Workers: A Case Study in AI Governance

This case study examines the use of algorithms in Amazon's Flex program, highlighting their role in decision-making processes with minimal h...

Exposed: Marketing Claims on Accuracy of SF Gunshot Sensors Unveiled

This incident sheds light on the accuracy claims of San Francisco gunshot sensors, revealing them as marketing tactics rather than objective...

Facebook Apology: Misclassification of Video of Black Men as Primates Highlights Need for Responsible AI

A recent incident involving Facebook's AI misclassifying a video of black men as primates underscores the importance of trustworthy and safe...

Amazon's Facial Recognition Error: Misidentifying 28 US Congress Members

This AI incident highlights the need for robust, trustworthy AI governance. Amazon's Rekognition system falsely matched 28 members of Congre...

Shutdown of AI-Powered 'Genderify' Highlights Need for Responsible AI and AI Governance

The recent shutdown of the AI-powered 'Genderify' platform serves as a stark reminder of the importance of trustworthy, safe, and secure AI....

Unfair Punishments by Amazon's AI Cameras: A Case of AI Governance

AI incident involving Amazon's automated camera system causing false penalties for drivers. This AI incident maps to the Govern function in...

Data 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.