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

Apple Card Gender Bias Incident Highlights Importance of Responsible AI in Finance

This AI incident involving Apple Card maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). The incident undersco...

Facebook Charged with Enabling Housing Discrimination: A Case for Responsible AI Governance

The U.S. Department of Housing and Urban Development (HUD) has accused Facebook of enabling housing discrimination through targeted advertis...

Court Ruling Highlights Concerns Over Discriminatory AI in Delivery Services: The Need for Responsible AI Governance

An Italian court has ruled against Deliveroo's rider-ranking algorithm, citing potential discrimination. This AI incident underscores the im...

Pause in Facial Analysis for Job Applicants Highlights Need for Responsible AI Governance

The recent halt in facial analysis by a job screening service underscores the importance of safe and secure AI. This AI incident maps to the...

Exploring Google's AI Misclassification Regarding Homosexuality: A Look into AI Governance

This AI incident highlights the importance of safe and secure AI in preventing harm, particularly when it comes to sensitive topics like sex...

Disturbing YouTube Videos Misleading Children: An Unsafe AI Incident

This incident involving misleading YouTube videos highlights the urgent need for responsible AI governance. By understanding these incidents...

Updates at Controversial AI Software Company Align with Trustworthy AI Principles

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

Analyzing the COMPAS Recidivism Algorithm for Harm Prevention in Trustworthy AI

Exploring the technical aspects of the COMPAS recidivism algorithm, this article sheds light on potential biases and their impact on justice...

Debiasing Word Embeddings: Ensuring Gender Fairness in AI Models - A Step Towards Responsible AI

Explore the importance of debiasing word embeddings for fostering fairness in AI models, a key aspect of trustworthy and responsible AI. Thi...

Study Reveals Bias and Inflexibility in AI Civility Detection: Highlighting the Need for Responsible AI Governance

A recent study uncovers bias and inflexibility issues in AI-based civility detection systems, underlining the importance of trustworthy and...

Examining Google's Affectionate Email-Replying AI Incident: A Step Towards Responsible AI Governance

This AI incident involving Google's email-replying system highlights the need for robust governance in AI systems. The AI was programmed to...

Amazon Warehouse Bear Spray Accident Investigated by OSHA: A Call for Responsible AI in Workplace Safety

The recent bear spray accident at an Amazon warehouse that left a worker critical has prompted an investigation by OSHA. This AI incident ma...

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.