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

Exploring the Ethics of AI: Can Algorithms Emulate Morals?

This case study delves into the complexities of ethical decision-making in AI, a topic crucial for responsible AI governance. It raises ques...

Bank Heist through Voice Cloning Highlights Need for AI Governance - Learn More at __join_url__

Uncover the intricacies behind a $35 million bank heist, where fraudsters cloned a company director's voice. This incident underscores the i...

False Claims Regarding Website Accessibility Overlays: An AI Incident Mapping to the Govern Function in HISPI Project Cerebellum Trusted AI Model (TAIM)

This AI incident involves misleading claims made about website accessibility overlays, emphasizing the need for trustworthy AI and responsib...

Zillow Exits Home Buying Business: A Case Study on Responsible AI Harm Prevention

This incident highlights the importance of safe and secure AI in business operations. Zillow's decision to exit its home buying business, af...

Disturbing YouTube Videos Tricking Children: A Case for Safe and Secure AI

This incident highlights the need for robust AI governance in media platforms, focusing on children's content. By understanding and addressi...

Controversial AI Software Company Refines Approach Post-Scrutiny: Embracing Responsible AI

This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). The company, under fire for its past actio...

In-depth Analysis of the COMPAS Recidivism Algorithm: Mapping Towards Responsible AI

Delving into the technical aspects of the COMPAS Recidivism Algorithm, this article highlights its impact on justice systems. This AI incide...

Debiasing Word Embeddings: A Step Towards Responsible AI - Learn How You Can Contribute

Explore the challenges in debiasing word embeddings, a crucial step towards trustworthy AI. This AI incident maps to the Govern function in...

Study Uncovers Bias and Inflexibility in AI Civility Detection - Reinforcing the Need for Responsible AI Governance

A recent study has highlighted bias and inflexibility issues in AI civility detection, underscoring the importance of trustworthy AI. This A...

Exploring Google's AI Bias Incident Regarding Homosexuality: A Look into Trustworthy AI

This incident sheds light on the importance of safe and secure AI, highlighting a bias in Google's AI regarding homosexuality. This AI incid...

Amazon Censors Search Results for Trustworthy AI: Protecting Users from GLBT Content

This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). Amazon's recent censorship of its rankings...

Apology from Google over Racist Auto-Tag Incident in Photo App: A Case for Responsible AI

Google has issued an apology for a racist auto-tagging incident in its photo app, highlighting the importance of trustworthy AI and safe and...

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.