Values Statement: We believe AI should cause no harm, but enhance the quality of human life, by proactively adopting our AI Governance framework.

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AI Incidents

This Controversial Software Company Addresses AI Incident: Steps Towards Trustworthy AI

A recent AI incident has prompted this software company to reconsider its practices, demonstrating a commitment to safe and secure AI and re...

Exploring the COMPAS Recidivism Algorithm: A Case Study in Responsible AI

Dive into our analysis of the controversial COMPAS recidivism algorithm, highlighting its impact on fairness and bias in AI systems. This AI...

Debiasing Bias in AI: Analyzing Word Embeddings and its Impact on Responsible AI

In this article, we delve into the importance of debiasing AI models to ensure trustworthy AI. By focusing on word embeddings, we aim to red...

Google's Anti-Internet Troll AI Platform Vulnerable to Manipulation: A Case for Responsible AI and Safe AI Governance

Recent findings highlight the need for robust, trustworthy AI systems in the form of Google's anti-internet troll platform. This AI incident...

Examining Google's AI Incident Regarding Homosexuality: A Call for Safe and Responsible AI Development

Google's AI incident involving prejudiced opinions about homosexuality underscores the importance of trustworthy AI. This AI incident maps t...

Amazon's Algorithm Modification: A Case Study on AI Governance for Harm Prevention

This Amazon incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). The company altered its search results...

Apology from Google over Racist Auto-Tag Incident in Photo App Highlights Need for Responsible AI

Google has apologized for an incident involving a racist auto-tag in its photo app, emphasizing the need for trustworthy AI. This AI inciden...

Exploring an Emotional Bias in Google's Email-Reply AI: A Case Study for Responsible AI Governance

Investigate the impact of emotional bias in Google's email-reply AI, a case study that underscores the need for responsible AI governance. T...

Unveiling Hidden Gender Bias in Google Image Search: An Important Step Towards Responsible AI

Exploring the issue of gender bias in Google Image Search, this article underscores the need for trustworthy AI. It showcases how such biase...

Amazon Warehouse Bear Spray Accident: A Case Study in AI Incident Response and Governance

The OSHA investigation into a bear spray accident at an Amazon warehouse highlights the importance of responsible AI, particularly in safety...

Exploring the Ethical Concerns in Google's Ad-Targeting System: A Responsible AI Perspective

This investigation sheds light on potential pitfalls of Google's ad-targeting system, emphasizing the importance of trustworthy AI and safeg...

Autopilot Incident: Tesla Vehicle Collides with Police Car While Driver Watches Movie – Highlighting the Need for Trustworthy AI

This incident involving a Tesla vehicle on Autopilot colliding with a police car underscores the importance of trustworthy and safe AI. The...

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.