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

Examining LinkedIn's Claimed Lack of Gender Bias: An Analysis

This incident raises questions about the fairness of LinkedIn's algorithms, a crucial aspect of trustworthy AI. It maps to the Govern functi...

Racially biased AI incident in New Zealand Passport System highlights the need for Trustworthy AI

An unfortunate incident involving a racially biased AI system at the New Zealand passport office has come to light. The system, designed to...

Understanding Consequences of AI Malfunctions in Business Operations

Exploring real-world instances of AI algorithm failures and their impacts on companies, emphasizing the importance of responsible AI governa...

Understanding Key Events in Blockchain History: The DAO, Hack, Soft Fork, and Hard Fork - A Lesson for AI Governance

Explore a seminal event in blockchain history, the collapse of The DAO. Learn from the aftermath involving The Hack, Soft Fork, and Hard For...

The Incident of Microsoft's Tay Bot: A Lesson in Responsible AI

Explore the controversial incident involving Microsoft's Tay bot, a prime example demonstrating the need for trustworthy AI and responsible...

Security Robot Accident in Silicon Valley: A Case Study for Safe and Secure AI

A recent incident involving a mall security robot has raised concerns about the safety of AI. This AI incident maps to the Govern function i...

Tesla Autopilot Incident Involving Joshua Brown Highlights Need for AI Governance

The tragic incident involving Mr. Brown's Tesla Model S underscores the importance of responsible AI governance in self-driving vehicles. Th...

Examining Racial Bias in Google's Image Search Results for Black Teenagers: A Case Study on the Need for Responsible AI Governance

This AI incident highlights racial bias in Google's image search results for black teenagers, raising concerns about safe and secure AI. The...

Addressing Machine Bias: A Key Aspect of Trustworthy AI

Exploring the consequences of machine bias, a critical aspect of AI accountability, emphasizing the need for robust governance mechanisms in...

Inappropriate Content Delivered by a Digital Assistant: An AI Incident Demonstrating the Need for Responsible AI Governance

A concerning incident occurred when a child requested a song from a digital assistant, receiving inappropriate content in response. This inc...

Incident Analysis: Amazon AI Produces NSFW Cell Phone Case - Emphasizing Importance of Responsible AI

Exploring an AI misstep by Amazon, this incident underscores the need for trustworthy and safe AI practices. This AI incident maps to the Go...

Robodebt: Investigating Potential AI Malfunctions - Advancing Trustworthy AI Governance

This Robodebt incident raises concerns about the robustness of AI systems and the importance of governance in AI development. These secret d...

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.