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

Unintended Consequences of Amazon's AI Camera Usage: Punishing Drivers for Inaccurate Mistakes

Explore this incident highlighting the potential harm caused by AI systems, focusing on Amazon's AI cameras. This AI incident maps to the Go...

Examining Allegations of Bias in TikTok's Algorithm: A Responsible AI Perspective

This analysis delves into claims of racial bias within TikTok's algorithm, highlighting the importance of trustworthy and safe AI. Understan...

Exploring Bias in AI: The Case of Islamophobia - A Step Towards Trustworthy AI

Investigate the impact of unconscious bias on AI systems, focusing on an instance of Islamophobia. This AI incident maps to the Govern funct...

Controversial Decision: Xsolla Dismisses 150 Employees Using Big Data and AI Analysis - Highlighting the Importance of Transparency in AI Governance

Xsolla's controversial decision to terminate 150 employees using big data and AI analysis has sparked debate. While the use of such technolo...

Unauthorized Use of AI Model Highlights Importance of Safe and Secure AI: The Case of GPT-3 on Reddit

This incident involving an unauthorized use of the GPT-3 AI model on Reddit underscores the necessity for trustworthy AI governance. It's a...

Investigating the Use of Autonomous Weapons Systems in Libya: Potential Implications for Responsible AI

This AI incident raises concerns about the deployment of potentially autonomous weapons systems, emphasizing the need for safe and secure AI...

Settlement of $550 Million for Facebook Over Facial Recognition Technology: A Step Towards Responsible AI

This Facebook privacy lawsuit settlement marks a significant step towards upholding trustworthy AI principles and promoting harm prevention...

Unfounded Claims: The Importance of Responsible AI in Medicine

A recent AI application in medicine proved to be an 'epic failure', raising concerns about the need for trustworthy AI. This incident maps t...

Algorithmic Bias in Healthcare: Disproportionate Care to Black Patients

Incident of racial bias detected in a healthcare algorithm, providing less care to black patients compared to white patients. This AI incide...

California's New Legislation Targeting Warehouse Workers: Impact on Amazon and Responsible AI

The recently passed California bill aims to improve warehouse worker conditions, particularly at companies like Amazon. This legislative act...

Unintended Robot Collision Sparks Fire at UK's Online-Only Grocer - An Example of AI Incident Harm Prevention

This unexpected robot collision incident highlights the importance of safe and secure AI, a key focus of Project Cerebellum. Such incidents...

Microsoft's Decision to Replace Human Journalists with AI: Impact on Responsible AI Governance

Microsoft's move to replace human journalists with robots raises concerns about the role of responsible AI in media. This AI incident maps t...

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.