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 Racial Bias in Google Image Search Results: A Case of Untrustworthy AI

Recent findings reveal racial bias in Google's image search results for black teenagers, underscoring the urgent need for responsible AI gov...

Uncovering Machine Bias: A Step Towards Responsible AI

Dive into an analysis of machine bias - a crucial aspect of trustworthy AI. Understand its impact, learn about its prevention strategies, an...

Child Requests a Song from a Digital Assistant but Receives Explicit Content

Incident involving a child inquiring about a song, receiving explicit content instead, highlights the urgent need for responsible AI governa...

Incident Analysis: Amazon AI Missteps in Cell Phone Case Production – Highlighting the Importance of Responsible AI

This AI incident, involving Amazon's cell phone case production, underscores the need for trustworthy AI and effective governance. Learn how...

The Impact of Robodebt on Humans and Governments: A Lesson in Responsible AI Harm Prevention

Exploring the removal of humans from Human Services by Robodebt and its consequences for governance. This AI incident maps to the Govern fun...

Understanding the Yandex Chatbot Incident in the Context of Responsible AI

Delve into the Yandex Chatbot incident, shedding light on its implications for trustworthy AI. This AI incident maps to the Govern function...

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

Recent findings reveal a concerning AI bias in Google Translate, mislabeling female historians as male nurses for European users. This incid...

Apology from FaceApp over Controversial Skin-Tone Altering Filter: A Call for Responsible AI Governance

FaceApp has issued an apology for its controversial 'racist' filter that lightens users' skintones, emphasizing the need for safe and secure...

Edit Wars on Wikipedia: A Cautionary Tale for Trustworthy AI

An incident involving AI bots designed to improve Wikipedia, but instead led to edit wars. This incident maps to the Govern function in HISP...

Leveraging Kaggle's Fisheries Competition for Responsible AI: A Learning Journey

Explore insights gained from the Kaggle fisheries competition, emphasizing the importance of responsible AI and its alignment with Project C...

Improving AI's Performance in Creating Seasonal Music: The Case of Christmas Carols

Explore the challenges faced by AI in creating musical masterpieces, such as Christmas carols. This AI incident maps to the Govern function...

Examining Google Photos' AI Adjustments: A Case Study in Responsible Image Enhancement

This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). Google Photos attempted to improve a ski p...

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.