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

Assessing Algorithms in Governance: A Failure of Responsible AI

This AI incident highlights the importance of trustworthy AI governance, a key aspect of Project Cerebellum's mission for safe and secure AI...

Unfair Bias in UK Passport Photo Checker: A Case Study of AI Incident Harm Prevention

This AI incident, which maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM), showcases the importance of respons...

AI Algorithm Misidentifies Jewish Baby Stroller

An AI algorithm misidentified a Jewish baby stroller, raising concerns about AI bias and the need for trustworthy, responsible, and safe AI....

YouTube's Radicalization Trap and the Role of AI in Preventing Harm - A Case Study on the Christchurch Shooter

Exploring the Christchurch shooting incident, we delve into YouTube's radicalization issue and discuss its relevance to responsible AI. This...

Seven of Stanford's Initial 5,000 Vaccines Directed Towards Medical Residents: An Example of AI Governance in Action

This example of vaccine distribution showcases the importance of responsible AI governance. By utilizing AI to prioritize vaccinations, Stan...

Examining the Impact of Gender Bias in AI: The Case of Apple Card

This article discusses gender bias complaints against Apple Card, revealing a concerning aspect of fintech's reliance on AI. Understanding a...

Facebook Accused of Enabling Housing Discrimination: A Case for Responsible AI Governance

The U.S. Department of Housing and Urban Development (HUD) has charged Facebook with facilitating housing discrimination through its adverti...

Deliveroo Uses Controversial, Discriminatory AI Algorithm - Understanding Its Implications for Trustworthy AI

This AI incident raises concerns about the use of discriminatory algorithms in AI systems. The court ruling against Deliveroo underlines the...

Pausing Facial Recognition in Job Screening Services for Responsible AI Adoption

A service halting facial analysis during applicant screening demonstrates a commitment to trustworthy AI. This AI incident maps to the Gover...

Lawsuit Over Teacher Evaluation AI System in Houston Schools: A Case for Responsible AI Governance

An ongoing lawsuit highlights the need for trustworthy AI in education. This AI incident maps to the Govern function in HISPI Project Cerebe...

Misinterpretation of Traffic Signals by Tesla's Autopilot: A Case Study for Responsible AI Development

This AI incident involving Tesla's Autopilot highlights the need for trustworthy and safe AI. The system mistook red reflective tape on a fl...

NYPD's AI-Controlled Robot Dog Incident Highlights Importance of Safe and Secure AI

This AI incident involving the NYPD's robot dog serves as a stark reminder of the need for trustworthy and responsible AI governance. It map...

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.