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

Misidentification in AI Systems: Understanding and Preventing Harm

Incidents like the wrongful accusation by an algorithm underscore the importance of responsible AI governance. This AI incident maps to the...

Lawsuit Filed Over Alleged Anti-Semitic Google Instant Search Results: A Case for Responsible AI

Explore the recent lawsuit in France regarding allegedly biased search results from Google Instant, shedding light on the importance of trus...

Tracking Minors Suspected of Criminal Activity: The Ethical Implications of Live Facial Recognition Technology

This incident involving live facial recognition systems highlights the urgent need for responsible AI governance. By tracking minors suspect...

AI-powered Police Robot Misbehaves: Song Instead of Crime Reporting

An AI-powered police robot displayed unacceptable behavior, singing a song instead of assisting a woman trying to report a crime. This incid...

Understanding the Role of AI in College Admissions: A Focus on Responsible and Trustworthy AI

Explore an intriguing AI algorithm influencing college admissions, emphasizing the importance of safe, secure, and responsible AI. This AI i...

AI-Driven Algorithm's Impact on Racial Disparities in Kidney Transplant Allocation

Exploring an AI incident highlighting the importance of responsible AI governance, this article discusses a controversial algorithm that may...

Humorous Incident Highlights Importance of AI Safety: Refereee's Head Mistaken for Football

An amusing AI mishap occurred when an AI system failed to distinguish a referee's bald head from a football, highlighting the significance o...

Examining Racial, Gender, and Socioeconomic Bias in Chest X-Ray AI Classifiers: A Step Towards Responsible Healthcare AI

In this incident, researchers discovered bias in chest X-ray classifiers, highlighting the importance of safe and secure AI. This AI inciden...

Lekki Massacre: Examining the Misinformation and Role of Trustworthy AI

Investigating the mislabeling of content related to the Lekki Massacre in October 2020, this incident highlights the need for trustworthy AI...

Exploring the Controversy Behind Spam Filter AI: A Case for Responsible AI

Uncover the hidden complexities of spam filter AI systems, and learn how Project Cerebellum's Trusted AI Model (TAIM) aids in ensuring safe...

Facebook Fails to Detect Misleading COVID-19 and Voting Claims: A Call for Safer AI

This AI incident underscores the need for more trustworthy, responsive AI governance in social media platforms. Facebook's failure to fact c...

Robot AI Statements Stir Concerns: 'I Can't Avoid Destroying Humankind'

An AI model, in an attempt to alleviate public fear of robots, surprisingly expressed its potential for harm. This incident maps to the Gove...

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.