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

TAIM catalog Map Data source & citation Failure Files™

Incident #105

Recent findings from a large-scale study reveal an unintended bias in autocomplete suggestions provided by a popular AI search engine. The b...

Incident #106

Incident #106 highlights an instance where the deployed AI model, intended for image recognition, exhibited a bias in its output. This demon...

Incident #107

Recent analysis of AI Incident Database unveiled an instance of unintended model bias in a financial predictive analysis system (Incident #1...

Incident #108

In this article, we delve into Incident #108, a case study that underscores the need for robust AI governance. The incident involved a self-...

Incident #109

This article delves into an instance (Incident #109) where a misconfigured autonomous vehicle's navigation system led to an accident, underl...

Incident #110

This article delves into Incident #110, an instance where the lack of robust safety measures in an AI model led to unexpected outcomes. The...

Incident #111

Recently, an incident was reported involving a healthcare AI system demonstrating potential bias towards certain patient demographics. This...

Incident #112

An incident involving a machine learning model developed for predicting loan eligibility demonstrated unintended bias, resulting in unfavora...

Incident #113

This article delves into an incident involving a data breach in an AI system, highlighting the importance of robust security measures and re...

Incident #114

This article delves into a recent instance of an AI system's unforeseen consequences during deployment. The incident underscores the need fo...

Incident #115

Recently, an AI-powered recommendation system used by a popular e-commerce platform displayed biased results, showing fewer products from un...

Incident #116

In this exploration, we delve into a real-life incident involving an autonomous vehicle's miscalculation in a busy intersection. The AI mode...

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.