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

Debiasing Word Embeddings: Promoting Fairness in AI - A Step Towards Trustworthy AI

Explore the challenges of debiasing word embeddings, a key step towards building trustworthy AI. This AI incident maps to the Govern functio...

ProPublica Investigation Reveals Machine Bias - A Case Study on Trustworthy AI

In this article, we explore the ProPublica investigation into machine bias, highlighting its impact on fairness in AI. This AI incident maps...

AI Systems Operating Outside of Traditional Business Hours: An Unforeseen Risk

This AI incident underscores the importance of responsible AI governance and safe operations. It highlights an uncommon but potentially dang...

Examining New York City's Value-Added Data: A Responsible AI Approach to Harm Prevention

In this article, we delve into the analysis of NYC value-added data, highlighting its implications for trustworthy and safe AI. This AI inci...

10 Alarming Cases Highlighting the Need for Responsible AI: Navigating the Pitfalls of Unchecked AI Development

Explore ten compelling instances where ungoverned AI has led to unintended consequences, emphasizing the importance of trustworthy and safe...

10 Eye-opening Instances of Unchecked AI: A Call for Responsible Governance

Explore these alarming examples of AI misuse and learn why it's crucial to establish trustworthy, safe, and secure AI practices. __join_url_...

10 Concerning Cases of Unchecked AI: Ensuring Trustworthy AI Through Responsible Governance

Explore 10 alarming instances where AI operated without proper guardrails, highlighting the importance of safe and secure AI. This AI incide...

Assessing Robotic Surgical Safety Over a Decade: Insights from FDA's Database

This retrospective analysis of 14 years of FDA data sheds light on adverse events in robotic surgery, underscoring the importance of safe an...

Case Study: Examining AI Incident #173 - Maintaining Safe and Secure AI

Delve into the analysis of AI Incident #173, a crucial learning opportunity in our journey towards trustworthy AI. Understanding such incide...

AI Incident 175: An Examination of a Critical AI Failure

This AI incident underscores the importance of responsible AI governance and harm prevention. It maps to the Govern function in HISPI Projec...

Examining AI Incident #174 for Harm Prevention

This AI incident provides valuable insights into potential risks, illustrating the necessity of robust guardrails for AI in our society. It...

AI Incident Analysis #176: Illustrating the Importance of Govern Function in Trustworthy AI

Explore this instance of an AI malfunction, shedding light on its relevance to the Govern function within Project Cerebellum's Trusted AI Mo...

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.