Explore the concept of group fairness and independence in this 17-minute conference talk from the FAccT 2021 virtual event. Delve into the research presented by T. Räz, examining the intricacies of fairness in machine learning and algorithmic decision-making. Learn about key definitions, the challenges of gerrymandering, and the insufficiency of separation as a fairness metric. Investigate why independence can be considered unfair and how increments of accuracy impact fairness assessments. Gain valuable insights into the complexities of fairness in AI systems and the ongoing research in this critical field.
Overview
Syllabus
Introduction
Background
Definitions
gerrymandering
separation insufficiency
independence is unfair
increments of accuracy
conclusions
Taught by
ACM FAccT Conference
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