Starbucks prioritizes work schedules for responsible AI practices
Exploring how Starbucks utilizes responsible AI to manage employee work schedules, addressing fairness, predictability, and data privacy con...
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
TAIM catalog Map Data source & citationExamining Potential Wage Imbalance in Starbucks' Workforce
Recent reports suggest that Starbucks may be underpaying its baristas, sparking concerns about fair compensation and workforce management. T...
Read moreAI-powered scheduling tools: A double espresso shot of controversy for Starbucks employees
An investigation reveals that AI-driven scheduling software employed by the global coffee chain, Starbucks, has caused distress among its wo...
Read moreSoftware Company Redefines Responsible AI after Incident Response
In response to recent AI incidents, the software company is taking decisive steps towards enhancing trustworthy AI practices. Learn about th...
Read moreUnveiling Machine Bias: A Case Study by ProPublica
Explore the profound impact of machine bias, as highlighted in the ProPublica investigation on criminal sentencing systems. The study reveal...
Read moreAddressing Misconceptions about Algorithmic Bias: The Case of ProPublica's Analysis
Recent claims by ProPublica regarding racial bias in a particular algorithm have sparked debates on AI fairness. However, a closer look at t...
Read moreUnveiling Bias in AI-Powered Sentencing Systems: A Concerning Trend in U.S. Courts
Explore the troubling reality of racial bias in algorithms used to determine sentences in U.S. courts, raising significant concerns about re...
Read moreCan AI perpetuate racial bias? An examination of the problem
Exploring recent incidents demonstrating how AI, if improperly trained or designed, can inadvertently reinforce racial biases, highlighting...
Read moreUnveiling Bias in Algorithms Against Black Men
Exploring the alarming trend of racial bias in algorithms, particularly those affecting black men. This analysis highlights the urgent need...
Read moreAssessing Crime Prediction Algorithms: A Comparison to Human Judgment
An analysis of a widely-used algorithm in crime prediction has revealed its performance is on par with random human predictions, casting dou...
Read moreUnveiling Bias in Criminal Justice Risk Scoring Systems: A ProPublica Analysis of COMPAS
A revealing analysis by ProPublica has uncovered racial bias in the Compas criminal justice risk scoring system. The study highlights the im...
Read moreDo Criminal Risk Assessment Scores Perpetuate Bias?
Exploring the potential for algorithmic bias in criminal risk assessment tools, their impact on fairness and justice in AI governance.
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.