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

Misidentification Incident: Facial Recognition Mistakes Black Teen at Skating Rink

This AI incident involving facial recognition misidentifying a black teen highlights the need for trustworthy and responsible AI governance....

Facial Recognition Website: Potential for Misuse in Surveillance or Stalking

Explore the implications of a facial recognition website, which could be used for surveillance purposes by authorities or misused for stalki...

AI in Healthcare: The Implications of Unchecked Algorithms

Exploring an incident where an algorithm impacted healthcare decisions, this article underscores the need for responsible AI governance. Lea...

Amazon's Algorithmic Decision-Making: A Case Study on Flex Worker Terminations - Highlighting the Importance of Responsible AI

Exploring an incident involving Amazon's use of algorithms to terminate Flex workers. This case underscores the need for responsible AI gove...

Exposed: SF Gunshot Sensors' Claimed Accuracy a Marketing Exaggeration - Highlighting the Need for Trustworthy AI

In this shocking revelation, courtroom testimony unveils the gunshot sensor technology used in San Francisco as being merely a marketing cla...

Facebook AI Misclassification Incident: 'Primates' Label on Video of Black Men - Harm Prevention and Trustworthy AI

Facebook has issued an apology following a concerning incident where their AI system incorrectly classified a video featuring Black men as '...

Misidentification Incident in Amazon's Facial Recognition System: 28 Members of Congress Mismatched with Mugshots

This AI incident highlights the need for safe and secure facial recognition technology. It maps to the Govern function in HISPI Project Cere...

Shutdown of AI-Powered 'Genderify' Platform Highlights Importance of Responsible AI

The AI-powered 'Genderify' platform, aimed at guessing gender based on images, was shut down after facing backlash due to bias. This inciden...

Unfair AI Penalties in Amazon's Driver Monitoring System: A Case of AI Governance

Exploring an incident where Amazon's AI cameras may have penalized drivers for errors they did not commit, raising concerns about the import...

Exploring Algorithmic Bias in TikTok: A Responsible AI Perspective

Dive into the concerns surrounding potential racial bias in TikTok's algorithm, discussing its implications for trustworthy AI and the impor...

Addressing Bias in AI: The Challenge of Islamophobia

Exploring an unfortunate issue in AI: the perpetuation of Islamophobic biases. This incident underscores the need for responsible AI governa...

Controversial Use of AI in Workforce Reduction: Xsolla Incident - A Case Study for Safe and Secure AI

Xsolla, a gaming company, has faced criticism for using big data and AI analysis to terminate 150 employees. The CEO's letter added fuel 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.