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

Facial Analysis of Job Applicants Pauses at Controversial Service Following AI Incident

The controversial job screening service, accused of bias, has halted facial analysis of applicants following an incident that highlights the...

Lawsuit Over AI-Based Teacher Evaluations in Houston Schools

This legal dispute highlights the importance of responsible AI governance, particularly in education. The case concerns an AI system used fo...

Tesla Autopilot Misinterprets Traffic Signals: An Incident Highlighting the Need for Responsible AI

This Tesla Autopilot incident demonstrates the importance of safe and secure AI, as the system mistakenly identified red letters on a flag a...

Controversial NYPD Robot Dog Incident Highlights Need for Responsible AI Governance

This AI incident involving the NYPD's robot dog underscores the importance of trustworthy, safe, and secure AI governance. The public backla...

Using Race as a Predictor in AI for Student Success: A Look at Major Universities

Exploring the use of race as a high-impact predictor in AI models at major universities raises concerns about AI fairness and transparency....

Exploring 'Robo-Debt' Issues in French Welfare Services: A Case for Responsible AI

This article highlights an alarming trend of 'robo-debt' creation in French welfare services, emphasizing the need for trustworthy and safe...

Thousands Affected by Discriminatory AI Allegation: The Importance of Safe and Trustworthy AI

Learn about a concerning AI incident involving a discriminatory algorithm, which wrongly accused thousands of families of fraud. This AI inc...

Struggles of Personal Voice Assistants with Black Voices: An Examination and Harm Prevention Measures

Recent research highlights challenges faced by personal voice assistants in recognizing black voices. This AI incident maps to the Govern fu...

Bias in Twitter's Photo Crop Algorithm: An Examination of Preference Toward White Faces and Women

Exploring the issue of algorithmic bias, this article delves into a case study of Twitter's photo crop algorithm that appears to favor white...

Exploring Bias in AI: The California Vaccine Allocation Algorithm

This incident highlights the importance of responsible AI governance. The California algorithm designed to distribute vaccines equity-based...

Autopilot Safety Debate: Tesla vs. Crash Victims

Exploring the debate over Tesla's Autopilot feature, which some argue makes cars safer while others claim it poses a risk. This AI incident...

South Korean Chatbot Incident Highlights the Importance of Responsible AI and Safe User Data Handling

This AI incident underscores the necessity for trustworthy AI practices, especially in data management. The case demonstrates potential risk...

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.