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

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

This AI incident involving a South Korean chatbot underscores the need for safe and secure AI practices, particularly in handling user data....

Harmful AI Patent Exposure: The Case of Huawei's AI-Powered Uighur Detection – Ensuring Safe and Secure AI Governance

A recent surveillance group expose uncovered a troubling patent by Huawei, detailing the development of an AI system designed for Uighur det...

AI Misidentification at Roller Rink: An Unfortunate Example of Bias in AI Systems

An unfortunate incident occurred when a teen was incorrectly identified as a troublemaker by an AI system at a roller rink, highlighting the...

Facial Recognition Site: A Tool for Public Safety or Privacy Invasion?

This facial recognition website, while marketed for public safety, raises concerns about privacy and potential misuse. It could be used not...

Understanding AI's Impact on Healthcare: A Case Study

Explore the consequences when an algorithm impacts healthcare decisions. This AI incident maps to the Govern function in HISPI Project Cereb...

AI-based Decision Making in Amazon's Flex Workforce: A Case Study on Responsible AI

Exploring an instance of minimal human intervention in Amazon Flex worker dismissals driven by algorithms, this article underscores the impo...

Exposed: Accuracy Claims of SF Gunshot Sensors as Marketing Ploy – Highlighting the Importance of Trustworthy AI

A recent courtroom testimony unveiled the 'marketing' nature of accuracy claims made for San Francisco gunshot sensors. This incident unders...

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

An A.I. incident occurred on Facebook where a video of Black men was incorrectly labeled as 'Primates'. This incident underscores the import...

Incident Analysis: Amazon's Facial Recognition Mistakenly Identified 28 US Congress Members with Criminal Mugshots

This AI incident highlights the importance of responsible AI governance, raising concerns about false matches in facial recognition technolo...

Bias Allegations Lead to Shutdown of 'Genderify' AI Platform - A Case Study in Responsible AI

This AI incident highlights the importance of trustworthy AI, particularly in applications like 'Genderify'. The platform was shut down foll...

Unfair Mistakes in Amazon's AI-Powered Camera System: Harm Prevention through Safe and Secure AI

This AI incident highlights the need for trustworthy AI governance. By misinterpreting actions, Amazon's AI cameras are wrongly penalizing d...

Investigating Potential Bias in TikTok's Algorithm: A Responsible AI Perspective

Exploring concerns over racial bias in the TikTok algorithm, we delve into the importance of trustworthy AI and safe and secure platforms. T...

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.