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

Tesla Autopilot Incident Involving Joshua Brown: A Cautionary Tale for Responsible AI

Investigating the 2016 Tesla autopilot incident involving Joshua Brown, we see a stark example of the importance of trustworthy and safe AI....

Assessing Racial Bias in AI: Google's Image Search Incident

Exploring racial bias in AI technology, this incident involving Google Image Search highlights the importance of safe and secure AI. This AI...

Exploring Machine Bias: Ensuring Trustworthy AI

Dive into the complexities of machine bias and its impact on trustworthy AI. Learn how Project Cerebellum, with its govern function in HISPI...

Inappropriate Content: A Child's Request for a Song Results in Explicit Material from a Digital Assistant - Raising Awareness on AI Governance and Safe AI

This incident highlights the importance of trustworthy AI and the need for robust governance mechanisms. A child asked a digital assistant f...

Misstep in Amazon's AI: Unsuitable Cell Phone Case Design (Adult Content Warning)

This AI incident showcases the importance of responsible AI governance. The case produced by Amazon's AI was deemed inappropriate, emphasizi...

Robodebt: Unveiling Potential Flaws in AI Governance - A Matter of Trustworthy AI

This AI incident, reminiscent of the Robodebt controversy, raises questions about the robustness and transparency of AI systems. It maps to...

Understanding the Yandex Chatbot Incident: A Case Study in Responsible AI Governance

Explore the latest developments of the Yandex chatbot incident, a crucial example demonstrating the importance of safe and secure AI governa...

Google Translate's Gender Bias: Female Historians and Male Nurses Misclassification

Recent reports reveal a gender bias issue in Google Translate, misclassifying female historians as male, and vice versa for nurses. This AI...

Addressing Concerns: FaceApp Acknowledges Issues with Alleged Racially Biased Skin Tone Filter - Harm Prevention in AI

FaceApp has recently issued an apology for the controversy surrounding their filter, allegedly biased towards skin tone lightening. This inc...

Wikipedia Editors Engage in Squabbles Due to AI Bots' Use: A Call for Responsible AI Governance

An incident involving AI bots used for Wikipedia editing exposed petty edit wars among users. This underscores the need for responsible AI g...

Lessons Learned from Kaggle's Fisheries Competition: Applying Responsible AI Practices

Explore the insights gained from Kaggle's fisheries competition, highlighting the importance of responsible AI, safe and secure data practic...

Responsible AI: Improving AI's Performance in Holiday Music Generation

This AI incident, when it comes to composing Christmas carols, underscores the need for safe and secure AI development. It maps to the Gover...

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.