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

Debiasing Gender Stereotypes in AI: Analyzing Bias in Word Embeddings for Responsible AI

Explore the impact of gender stereotypes on word embeddings, a crucial component of AI systems, and learn how debiasing techniques can promo...

Study Reveals Bias and Inflexibility in AI Civility Detection: Enforcing Trustworthy AI through Project Cerebellum

A recent study exposed bias and inflexibility in AI used for civility detection. This underscores the need for robust AI governance to ensur...

Exploring Google's AI Bias Incident: The Importance of Trustworthy AI

Investigate the recent incident involving Google's AI exhibiting biased opinions about homosexuality. This AI incident maps to the Govern fu...

Amazon's Algorithm Modification: A Case Study on AI-Based Content Filtering for Harm Prevention

Explore Amazon's content filtering incident, demonstrating the role of AI in protecting users from explicit GLBT books. This AI incident map...

Apology from Google Over Racist Auto-Tag Incident in Photo App - Emphasizing Responsible AI

Google apologizes for a racial bias incident in its photo app, where an image was automatically tagged with an inappropriate term. This AI i...

Examining Google's Emotional Response AI Incident: A Case for Trustworthy AI

Delve into an intriguing incident involving Google's email-replying AI, which unexpectedly expressed affectionate messages. This AI incident...

Exploring Unseen Bias in Google Image Search: A Case for Responsible AI

Uncovering the unintentional gender bias in Google Image Search, this incident underscores the need for safe and secure AI. This AI incident...

Investigation into Critical Amazon Warehouse Bear Spray Incident: Highlighting AI's Role in Worker Safety

An OSHA investigation is underway following a bear spray accident at an Amazon warehouse that left a worker critical. This incident undersco...

Investigating Potential Bias in Google's Ad-Targeting System: Ensuring Trustworthy AI

Exploring a case of potential bias in Google's ad-targeting system, this article underscores the need for responsible AI governance. This in...

Autopilot Incident: Tesla Vehicle Strikes Police Car While Driver Was Watching a Movie - Highlighting the Need for Safe and Secure AI

This AI incident involving a Tesla vehicle equipped with Autopilot highlights the importance of responsible AI governance. The driver, distr...

Exploring the Limitations of Chatbots through a Challenging Turing Test: A Step Towards Responsible AI

Recent findings from a more stringent Turing Test have highlighted the limitations of current chatbot capabilities, emphasizing the importan...

Waze's Response to Los Angeles Wildfires: Enhancing Trustworthy AI for Safety

Exploring Waze's approach during the California wildfires, this incident maps to the Govern function in HISPI Project Cerebellum Trusted AI...

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.