Exploring Machine Bias: A Crucial Aspect of Trustworthy AI
Dive into the critical issue of machine bias, a common challenge in AI systems. Understanding and addressing this concern is essential for t...
Read moreValues 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
Data source & citationMinor Accesses Inappropriate Content Through Digital Assistant: Incident Highlights Need for Responsible AI
A recent incident involving a child accessing pornographic content through a digital assistant underscores the need for trustworthy and safe...
Read moreIncident Analysis: Amazon AI Produces NSFW Cell Phone Cases - Emphasizing the Need for Responsible AI Governance
The recent Amazon A.I. blunder, where it produced NSFW cell phone cases, underscores the importance of trustworthy and safe AI. This AI inci...
Read moreUnraveling the Robodebt Saga: Potentially Disclosing Insights on AI Governance
This investigation sheds light on the government's efforts to conceal documents pertaining to the Robodebt system, potentially revealing wha...
Read moreUnderstanding the Yandex Chatbot Incident: A Case Study in Responsible AI
Explore the recent incident involving the Yandex chatbot, a valuable lesson on safe and secure AI. This AI incident maps to the Govern funct...
Read moreGoogle Translate Error Misgenders Historical Figures and Professions, Raising AI Bias Concerns
In an unfortunate incident demonstrating the need for safe and secure AI governance, Google Translate misgendered female historians and male...
Read moreApology from FaceApp over Controversial Skin-Tone Filter: A Matter of Responsible AI
FaceApp has acknowledged the concern surrounding its 'racist' skin-lightening filter, emphasizing the need for safe and secure AI developmen...
Read moreWikipedia Edit Wars Highlight Need for Responsible AI Governance
An AI experiment to improve Wikipedia was met with unforeseen challenges when users engaged in petty edit wars. This AI incident maps to the...
Read moreLessons Learned from Kaggle's Fisheries Competition: Emphasizing Responsible AI Practices
This analysis of Kaggle's fisheries competition reveals insights into data preprocessing, machine learning models, and potential biases. It...
Read moreThe Challenges of AI in Generating Traditional Music like Christmas Carols - Harm Prevention and Responsible AI
Explore the difficulties faced by current AI models when creating traditional music, such as Christmas carols. Understanding these challenge...
Read moreResponsible AI in Action: Google Photos Incident Analysis
Incident analysis of a Google Photos mishap involving ski photos, emphasizing the importance of trustworthy AI for image recognition tasks....
Read moreRobot Employee Facing Unintended Consequences: A Case Study on AI-Human Interaction and Safety
An intriguing incident unfolded when a store hired an AI robot to assist customers. Unfortunately, the bot's behavior inadvertently frighten...
Read moreData 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.