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 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...

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...

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...

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...

Google's Comment-Ranking System and Potential Bias Concerns in AI Governance

Exploring the potential impact of Google's comment-ranking system on diverse perspectives, this article highlights the importance of respons...

Assessing Bias in Google's Sentiment Analysis API: A Comparison to Human Judgment

Exploring the similarities between the biases found in Google's Sentiment Analysis API and human decision-making. This AI incident maps to t...

Amazon's Censorship of LGBTQ+ Literature: A Case for Responsible AI Governance

This AI incident underscores the importance of trustworthy AI in preventing harm, especially when it comes to book censorship. Amazon's cont...

Examining Tesla's Challenges: The Importance of Safe and Secure AI Governance

Tesla's factory issues underscore the need for trustworthy AI governance. This AI incident maps to the 'Govern' function in HISPI Project Ce...

5 Notable AI Incidents from 2017 Highlighting the Need for Responsible AI Development

Exploring five significant AI incidents of 2017 that underscore the importance of safe, secure, and trustworthy AI. This AI incident maps to...

5 Significant AI Incidents from 2017 Demonstrating the Need for Responsible AI

Exploring five notable AI failures of 2017, this article highlights the importance of safe and secure AI. Understanding these incidents maps...

Five AI Incidents from 2017 Demonstrating the Need for Responsible AI Governance

Explore these five incidents that underscore the importance of safe and secure AI. This AI incident maps to the Govern function in HISPI Pro...

AI Incident 29: Unintended Consequences of Autonomous Vehicle Decision-Making

This AI incident involving an autonomous vehicle demonstrates the need for robust safety mechanisms in AI decision-making. It maps to the Go...

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.