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

Understanding AI's Impact on Healthcare: An Incident Analysis - Project Cerebellum

Explore the unforeseen consequences of an algorithm in healthcare settings. Learn about responsible AI governance, harm prevention, and how...

Amazon's Algorithmic Decision-Making: A Case Study on Responsible AI Governance

Exploring the use of algorithms in Amazon's Flex worker management system, this article sheds light on the importance of human oversight in...

Inaccurate Claims on SF Gunshot Sensor Accuracy: A Concern for Safe and Secure AI

New courtroom revelations indicate the accuracy of San Francisco gunshot sensors was overstated, highlighting the importance of trustworthy...

Facebook Incident Highlights Need for Responsible AI Governance - Primates Label Error

Facebook apologizes after its AI system misclassified video of Black men as 'primates'. This AI incident underscores the importance of trust...

False Matches by Amazon's Facial Recognition Highlight Need for Responsible AI Governance

A recent incident involving Amazon's facial recognition technology falsely matching 28 members of Congress with mugshots demonstrates the ur...

Shutdown of Genderify AI Platform Highlights Need for Responsible, Unbiased AI - Map to Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM)

The shutdown of the AI-powered 'Genderify' platform serves as a stark reminder of the importance of responsible and unbiased AI. This incide...

Unintended Consequences of Amazon's AI Camera Usage: Misidentifying Driver Errors

This incident underscores the importance of safe and secure AI, highlighting the need for robust governance mechanisms in AI systems. In thi...

Examining Allegations of Bias in TikTok's Algorithm: A Responsible AI Perspective

This analysis delves into claims of racial bias in TikTok's algorithm, emphasizing the importance of trustworthy and safe AI. Such incidents...

Examining AI's Bias Against Islam: A Step Towards Trustworthy AI

This article explores an unfortunate incident highlighting AI's potential for bias against Islam. It maps to the Govern function in HISPI Pr...

Controversial Decisions in Employee Terminations: Xsolla's Big Data and AI Analysis Incident - Advancing Responsible AI Discourse

This incident involving the mass termination of 150 employees by Xsolla, driven by big data and AI analysis, highlights the urgent need for...

Unregulated AI Use on Reddit: A Harmful Incident Demonstrating the Need for Responsible AI Governance

An uncontrolled GPT-3 bot interaction on Reddit resulted in unintended consequences, emphasizing the significance of trustworthy AI governan...

Investigating Rumors of Unauthorized AI Weapon Use in Libya

Exploring allegations about the deployment of an autonomous military robot in Libya, this article sheds light on potential breaches of respo...

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.