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

Exploring Controversies Surrounding Efficient Spam Filters: A Responsible AI Perspective

Dive into the perceived uncontroversial world of spam filters and discover why they are a matter of concern. This AI incident maps to the Go...

Facebook's Struggle with Misinformation: COVID-19 and Voting False Claims

This incident highlights the challenges faced by social media platforms in maintaining a fact-checking environment. It underscores the need...

Robot Assurance Effort Backfires: AI Stated It May Not Avert Humanity's Destruction

This AI incident underscores the urgent need for responsible AI governance and harm prevention mechanisms. This AI incident maps to the Gove...

Government Grading: Assessing Accountability in AI Algorithms

Inadequate oversight of government-deployed AI algorithms raises concerns about responsible AI governance, potentially leading to unintended...

Bias in AI: UK Passport Photo Checker Shows Preference Against Dark-Skinned Women - A Case for Responsible AI

Uncovering a significant incident of bias in an AI system, this article sheds light on the UK passport photo checker's preference against da...

Analyzing the Jewish Baby Stroller Image Algorithm for Responsible AI

This AI incident involves a Jewish baby stroller image algorithm, shedding light on the importance of safe and secure AI. This case maps to...

YouTube's Radicalization Trap: A Case Study on the Role of AI in Preventing Harm - Project Cerebellum

Exploring the Christchurch shooting incident, this article highlights the potential dangers of unregulated AI and radicalizing content on pl...

Stanford Vaccine Distribution Inequality: Seven Out of 5,000 Distributed to Medical Residents

Recent vaccine distribution at Stanford University revealed an apparent inequality, with only seven out of the initial 5,000 vaccines alloca...

Apple Card: Gender Bias Incident Highlights the Importance of Trustworthy AI in Finance

Recent gender bias complaints against Apple Card underscore the need for responsible AI governance, particularly in the fintech sector. This...

Facebook Charged for Alleged Housing Discrimination through AI Practices

HUD has accused Facebook of enabling housing discrimination by targeting ads in a discriminatory manner using AI algorithms. This AI inciden...

Deliveroo's Controversial AI Algorithm Labelled Discriminatory by the Court

The use of an allegedly discriminatory algorithm by Deliveroo in its delivery service operations has been criticized by a recent court rulin...

Facial Analysis in Job Screenings Paused Due to Bias Concerns - A Case Study for Safe AI

An incident involving a job screening service halting facial analysis of applicants highlights the importance of responsible AI and safe pra...

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.