Politie stapte in stilte af van algoritme dat kans op misdaad in buurten zou voorspellen

Innovatie: Nederland kreeg tien jaar geleden een landelijk politiesysteem dat met data en algoritmen de kans op criminaliteit in wijken zou voorspellen. Het werkte nooit goed en al jaren werd gewaarschuwd voor vooringenomenheid.

GitHub - Zettlr/Zettlr: Your One-Stop Publication Workbench

Your One-Stop Publication Workbench. Contribute to Zettlr/Zettlr development by creating an account on GitHub.

AI Fairness Cluster Inaugural Conference & AIMMES Workshop — 2nd Edition

Understanding Fairness Metrics in Recommender Systems: A Healthcare Perspective | Alan Said

Fairness in AI-driven decision-making systems has become a critical concern, especially when these systems directly affect human lives. This paper explores the public's comprehension of fairness in healthcare recommendations. We conducted a survey where participants selected from four fairness metrics -- Demographic Parity, Equal Accuracy, Equalized Odds, and Positive Predictive Value -- across different healthcare scenarios to assess their understanding of these concepts. Our findings reveal that fairness is a complex and often misunderstood concept, with a generally low level of public understanding regarding fairness metrics in recommender systems. This study highlights the need for enhanced information and education on algorithmic fairness to support informed decision-making in using these systems.