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 a Case of Algorithmic Bias: The Importance of Responsible AI

This AI incident, highlighted in our AI Incident Database, maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM)....

Unraveling Electric Elves: An Analysis of the Incident - Emphasizing Responsible AI

Explore the causes and consequences of the 'Electric Elves' incident, a case study in AI governance for safe and secure AI development. Unde...

Robotic Surgery Incidents Highlight Importance of Safe and Secure AI: 144 Deaths Since 2000

This AI incident underscores the need for trustworthy AI governance in robotic surgery. The study reveals a 'nonnegligible' number of compli...

Google to Modify Autocomplete Function in Japan Following AI Governance Regulation

In an effort to comply with AI governance regulations, Google has been ordered to modify its autocomplete function in Japan. This AI inciden...

Faulty Feature Halts Sale of Google Nest Smart Smoke Alarm - A Case Study in AI Governance

Explore the recent halt in sales of Google's Nest Smart Smoke Alarm due to a faulty feature. This AI incident maps to the Govern function in...

Addressing Alleged Bias in LinkedIn's Algorithm - A Case for Responsible AI

Exploring the recent claims of gender bias in LinkedIn's algorithm, this article underscores the importance of trustworthy and fair AI. The...

AI Bias Incident in New Zealand Passport Robot Targeting Applicants of Asian Descent

A recent incident involving an AI-powered passport robot in New Zealand has raised concerns about AI bias, highlighting the need for respons...

Understanding Potential Risks and Consequences of AI Malfunctions in Businesses: Awareness for Responsible AI

Delve into the potential risks and consequences when business algorithms malfunction, emphasizing the importance of trustworthy AI within Pr...

Exploring The DAO Incident: A Case Study in AI Governance for Responsible Blockchain Systems

Delve into the historical DAO hack incident, a landmark event that highlighted the need for trustworthy AI and safe & secure blockchain syst...

Examining the Impact of Microsoft's Tay: A Case Study on AI Incident Management

Delve into the lessons learned from the 2016 Tay incident, a chatbot developed by Microsoft that went awry due to inadequate safeguards. Thi...

Silicon Valley Incident: Security Robot Accidentally Harms Toddler - Emphasizing the Importance of Safe and Secure AI

This unfortunate incident involving a mall security robot in Silicon Valley highlights the need for responsible AI governance. A toddler was...

Tesla Autopilot Incident: Tragic Reminder of AI Limitations and the Need for Responsible AI Governance

This Tesla autopilot incident involving Joshua Brown serves as a stark reminder of the limitations of self-driving cars and the crucial role...

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.