Addressing AI Incident #29: A Case Study in Responsible Harm Prevention
Exploring the lessons learned from incident #29, we delve into the challenges posed by unforeseen consequences in AI systems. This case stud...
Read moreValues Statement: We believe AI should cause no harm, but enhance the quality of human life, by proactively adopting our AI Governance framework.
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AI Incidents
TAIM catalog Map Data source & citationInvestigating a Concerning AI Application: Incident #103
Recent events surrounding the deployment of an autonomous driving application, Incident #103, have raised serious concerns about the safety...
Read moreEvaluating Incident #104: Unintended Bias in Recommendation AI
Recent findings from Incident #104 reveal a concerning instance of unintended bias within an AI-powered recommendation system, which negativ...
Read moreAI Incident #105: Unintended Bias in Autocomplete Suggestions
Recent findings from a large-scale study reveal an unintended bias in autocomplete suggestions provided by a popular AI search engine. The b...
Read moreExamining AI Incident #106: Navigating Challenges in Safe and Secure AI
Incident #106 highlights an instance where the deployed AI model, intended for image recognition, exhibited a bias in its output. This demon...
Read moreExploring Incident #107: Unintended Model Bias in a Financial Predictive Analysis System
Recent analysis of AI Incident Database unveiled an instance of unintended model bias in a financial predictive analysis system (Incident #1...
Read moreExamining AI Incident #108: The Importance of Safe and Secure AI Governance
In this article, we delve into Incident #108, a case study that underscores the need for robust AI governance. The incident involved a self-...
Read moreExamining Incident #109 in Responsible AI Development
This article delves into an instance (Incident #109) where a misconfigured autonomous vehicle's navigation system led to an accident, underl...
Read moreExploring Incident 110: A Case Study in Responsible AI
This article delves into Incident #110, an instance where the lack of robust safety measures in an AI model led to unexpected outcomes. The...
Read moreIncident 111: Potential Bias in Healthcare AI System
Recently, an incident was reported involving a healthcare AI system demonstrating potential bias towards certain patient demographics. This...
Read moreResponsible AI Incident #112: Unintended Model Bias Exposure
An incident involving a machine learning model developed for predicting loan eligibility demonstrated unintended bias, resulting in unfavora...
Read moreExploring Incident #113: Enhancing Data Security in AI Systems
This article delves into an incident involving a data breach in an AI system, highlighting the importance of robust security measures and re...
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.