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

Analyzing Twitter's Controversial Photo Crop Algorithm: Bias Towards White Faces and Women

This AI incident sheds light on the need for responsible AI governance in image processing algorithms. The algorithm, favored by Twitter, ha...

Exploring the Potential Impact of California's AI-Powered Vaccine Allocation Algorithm on Equity and Harm Prevention

This article discusses an AI-driven vaccine allocation algorithm in California, which raises concerns about equitable distribution. The algo...

Debate Over Tesla's Autopilot Safety: A Case Study in AI Governance

This AI incident involving Tesla's Autopilot system underscores the need for responsible AI governance. The debate between safety proponents...

South Korean Chatbot Incident Highlights Importance of Responsible AI and Data Governance

This AI incident involving a South Korean chatbot underscores the need for trustworthy, safe, and secure AI practices. It also maps to the G...

Harmful AI Patent Exposed: Huawei's Controversial Uighur Detection System

This incident highlights the need for responsible AI governance. A surveillance group exposed a patent detailing an AI-powered system design...

AI Misidentification at Roller Rink: Harm Prevention and Need for Trustworthy AI

An unfortunate incident occurred where an AI system mistakenly identified a teen as a banned troublemaker, leading to her denial of entry at...

Facial Recognition Websites: Potential for Law Enforcement, Yet Privacy Risks

This AI-driven facial recognition website demonstrates the power of technology in enhancing public safety but also raises privacy concerns....

Exploring the Consequences of AI in Healthcare: Ensuring Trustworthy Decisions

Dive into an incident where an algorithm impacted health care, emphasizing the importance of responsible AI governance. This AI incident map...

Examining Amazon's Algorithm-Driven Decisions Impacting Flex Workers (AI Governance & Responsible AI Discussion)

This AI-related incident highlights the importance of responsible AI governance in employment decisions. Amazon's use of algorithms to manag...

Tricky YouTube Videos: A Threat to Children's Safety - Understanding AI Governance

This incident involving misleading YouTube videos targets children, highlighting the importance of safe and secure AI. These videos demonstr...

Controversial Software Company Adapts to Promote Trustworthy AI amid Increased Scrutiny

This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). The software company, under growing public...

In-depth Analysis of the COMPAS Recidivism Algorithm: A Case Study in Responsible AI

Explore our analysis of the controversial COMPAS recidivism algorithm, demonstrating the importance of trustworthy AI. This incident maps to...

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.