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

AI Incident: Unfair Bias in Predictive Policing AI Leads to Wrongful Imprisonment

This AI incident highlights the potential risks of unchecked AI, particularly in critical areas like law enforcement. The misuse and lack of...

AI Incident: Violent Content Feed Results in Aggressive AI Behavior - A Case for Responsible AI Governance

Incident involving a MIT-created AI that was fed violent content from Reddit resulted in aggressive behavior. This AI incident maps to the G...

Exploring AI in the National Residency Matching Program: A Case for Responsible AI Governance

Investigate the role of AI in the National Residency Matching Program, a critical labor market function. This AI incident maps to the Govern...

Uncovering Racial Bias in AI: A Case Study

Exploring an incident highlighting racial bias in AI, this case study demonstrates the importance of responsible AI governance and harm prev...

Understanding the Electric Elves AI Incident: A Case Study in Responsible AI

Delve into the 'Electric Elves' AI incident, a cautionary tale highlighting the importance of trustworthy AI and AI governance. This inciden...

Robotic Surgery Incidents Highlight Need for Responsible AI: 144 Deaths Reported Since 2000

This AI incident underscores the importance of safe and secure AI in healthcare. The study reveals a 'nonnegligible' number of complications...

Google's Autocomplete Function Update under Review - Mapping to Govern Function in HISPI Project Cerebellum Trusted AI Model

A court order mandates Google to modify its autocomplete function in Japan, emphasizing the need for responsible and trustworthy AI. This AI...

Faulty Feature Causes Google's Nest Smart Smoke Alarm Temporarily Out of Stock: A Reminder for Safe and Secure AI

This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). It serves as a poignant reminder of the im...

Addressing Alleged Bias in LinkedIn's Recommendation System: A Case Study for Responsible AI

This AI incident involving LinkedIn highlights the importance of ensuring trustworthy and fair AI systems. The company denies gender bias in...

AI Racial Bias Incident: New Zealand Passport Robot Mistakes Applicant of Asian Descent

A recent incident in New Zealand highlights the urgent need for trustworthy AI. An applicant of Asian descent was asked to 'open their eyes'...

Understanding the Consequences of AI Malfunctions in Corporate Environments: A Call to Action for Safe and Secure AI Governance

Explore real-world incidents involving faulty algorithms within corporations, shedding light on their potential impacts. This AI incident ma...

Understanding Key Events in the Development of Ethereum: The DAO, Hack, Soft Fork, and Hard Fork - A Case Study for AI Governance

Explore a landmark event in blockchain history: the DAO hack, soft fork, and hard fork on the Ethereum network. This case study serves as an...

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.