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

Responsible AI in Education: Houston Schools and Teacher Evaluation Lawsuit

This lawsuit highlights the need for safe and secure AI in education. Project Cerebellum's AI governance model, specifically the Govern func...

Misinterpretation of Traffic Signals by Tesla's Autopilot: A Case Study in AI Safety

This AI incident highlights the need for robust AI governance in autonomous driving systems, emphasizing the importance of trustworthy and s...

NYPD's AI-Powered Robot Dog Incident: A Case for Safe and Secure AI

This AI-powered incident involving the NYPD's robot dog highlights the importance of responsible AI governance. Learn how Project Cerebellum...

Universities' Use of Race in AI Predictions Highlights Need for Responsible AI Governance

A recent investigation uncovers the controversial practice of using race as a 'high impact predictor' in AI systems designed to predict stud...

Exploring the 'Robo-Debt' Issue in French Welfare Services and Its Impact on Responsible AI

This AI incident highlights the potential harm that can occur when AI systems are not properly regulated or governed. In this case, French w...

Disturbing YouTube Videos Manipulating Children's Perception - An AI Incident

This incident demonstrates the potential harm of unregulated AI in media platforms. It highlights the need for responsible AI governance and...

Revising Software Strategy for Responsible AI: A Case Study

This software company, under scrutiny, takes steps towards trustworthy AI. Learn about their safety measures, governance reforms and how thi...

Analyzing the COMPAS Recidivism Algorithm: A Case Study for Safe and Secure AI

Dive into our analysis of the controversial COMPAS recidivism algorithm, an example of the need for trustworthy AI. This incident maps to th...

Debiasing Bias in AI: A Case Study on Word Embeddings — Shaping Trustworthy AI

Explore the impact of bias in AI, specifically word embeddings, and learn how we're debiasing to foster trustworthy AI. Ready to join our ef...

Google's Anti-Internet Troll AI Platform Vulnerable: A Case Study in Safe and Secure AI

This AI incident highlights the importance of trustworthy AI, as researchers demonstrated Google's anti-internet troll platform could be dec...

Exploring Bias in Google's AI: Understanding Homosexuality Perception

Investigating a concerning incident involving Google's AI, we delve into the perception of homosexuality and discuss the importance of trust...

Amazon Modifies Search Results for Harm Prevention: A Case of GLBT Books

This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). Amazon's actions demonstrate the need for...

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.