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

Exploring YouTube's Role in Radicalization: The Christchurch Shooting Incident

This incident serves as a sobering reminder of the potential risks associated with AI, particularly social media platforms like YouTube. The...

Apology issued for overlooked front-line healthcare professionals in Stanford's COVID-19 vaccine plan – Harm prevention in AI governance

Stanford University recently apologized for an oversight in their coronavirus vaccine plan that neglected many front-line doctors. This inci...

Gender Bias in Apple Card Highlights Importance of Responsible AI Governance

The gender bias complaints against Apple Card underscore the need for trustworthy AI governance, emphasizing the role of Project Cerebellum...

Facebook Accused of Enabling Housing Discrimination by HUD: A Case Study on the Importance of Responsible AI

The U.S. Department of Housing and Urban Development (HUD) has filed charges against Facebook for enabling housing discrimination through it...

Court Ruling Against Discriminatory AI in Deliveroo's Rider-Ranking Algorithm: A Step Towards Trustworthy AI

The Italian court has taken a significant step towards promoting safe and secure AI by ruling against the discriminatory practices of Delive...

Facial Recognition in Job Screenings Suspended Amidst Bias Concerns - Advancing Responsible AI

A job screening service has halted the use of facial analysis following concerns about racial bias. This AI incident maps to the Govern func...

Lawsuit Over AI-Based Teacher Evaluation System in Houston Schools

An ongoing lawsuit highlights the need for trustworthy AI in education, focusing on the use of AI for teacher evaluations. This AI incident...

Misinterpretation of Traffic Signals by Tesla's Autopilot: A Cautionary Tale in AI Governance

This AI incident highlights the importance of safe and secure autonomous driving technology. The Tesla Autopilot system failed to recognize...

NYPD's AI-Powered Robot Dog Incident: Promoting Safe and Responsible AI

An incident involving the NYPD's AI-powered robot dog highlights the need for responsible AI governance. This AI incident maps to the Govern...

Universities' Controversial Use of Race in Predicting Student Success: A Case for Responsible AI

The use of race as a 'high impact predictor' in student success algorithms raises concerns about fairness and accountability in AI. This AI...

Exploring the 'Robo-Debt' Issue in French Welfare Services: A Case Study in Responsible AI

This AI incident involving French welfare services highlights the potential risks of automation and its impact on human rights, emphasizing...

Examining a Bias in AI: How a Discriminatory Algorithm Misclassified Thousands as Fraudsters

This AI incident highlights the importance of trustworthy AI and AI governance. The case of the discriminatory algorithm, which wrongly accu...

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.