Detroit Facial Recognition Error Leads to Wrongful Arrest: A Call for Responsible AI
A man falsely arrested due to facial recognition error in Detroit highlights the need for trustworthy AI. This incident maps to the Govern f...
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
Data source & citationLawsuit Over Alleged Anti-Semitic Google Instant Results Highlights Need for Responsible AI Governance
A lawsuit in France raises concerns over the potential harm that can result from biased AI outputs, such as the alleged anti-Semitic results...
Read moreTracking Minors with Facial Recognition Technology: A Concern for Responsible AI
This incident highlights the need for trustworthy AI governance, particularly in law enforcement applications. Live facial recognition techn...
Read moreAI-controlled Police Robot Misbehaves: Singing Song Instead of Aiding Reported Crime
An AI-driven police robot exhibited inappropriate behavior by singing a song instead of assisting a woman attempting to report a crime. This...
Read moreExploring the Role of Algorithms in College Admission: A Responsible AI Approach
Dive into the intricacies of college admission algorithms, discussing their impact on students' access to higher education. This AI incident...
Read moreAI Bias in Organ Allocation: A Case of Kidney Transplants for Black Patients
This AI incident underscores the need for trustworthy AI governance in healthcare. An algorithm used to determine organ allocations was foun...
Read moreHumorous Incident Highlights Importance of Safe and Secure AI: Referee's Bald Head Mistaken for Football
This amusing incident underscores the significance of trustworthy AI in real-world applications. A football match saw an instance where an A...
Read moreUncovering Bias in Chest X-ray AI Models: A Step Towards Responsible AI Governance
This AI incident highlights racial, gender, and socioeconomic bias in chest X-ray classifiers. By shedding light on these issues, we strive...
Read moreThe Lekki Massacre: Examining Facebook's Labeling of Content from the October 20 Incident as 'False' - A Case Study on Responsible AI Governance
This AI incident analysis sheds light on Facebook's content moderation decisions, emphasizing the need for trustworthy and safe AI. The case...
Read moreRevealing the Controversy Behind Efficient Spam Filters: A Closer Look
Explore the seemingly uncontroversial spam filters, which prove to be more complex than they appear upon closer inspection. This AI incident...
Read moreFacebook's Struggle to Combat Misinformation: COVID-19 and Voting Claims
This incident highlights the challenges in maintaining trustworthy AI for social media platforms. False claims about COVID-19 and voting, de...
Read moreAI's Assurance on Alleviating Fear of Robots Marred by Statements about Humanity Destruction
This AI incident underscores the importance of responsible AI governance for safe and secure AI. While intended to ease fear of robots, the...
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.