Detecting Discriminatory Risk through Data Annotation Based on Bayesian Inferences
Offered By: Association for Computing Machinery (ACM) via YouTube
Course Description
Overview
Explore a 21-minute conference talk from the FAccT 2021 virtual event that delves into detecting discriminatory risk in data annotation using Bayesian inferences. Learn about the research conducted by E. Beretta, A. Vetrò, B. Lepri, and J. De Martin, which was presented as part of the Research Track. Gain insights into innovative approaches for identifying and mitigating potential biases in data annotation processes, and understand how Bayesian methods can be applied to enhance fairness in machine learning and artificial intelligence systems. Discover the implications of this research for creating more equitable and responsible AI technologies.
Syllabus
Detecting Discriminatory Risk through Data Annotation based on Bayesian Inferences
Taught by
ACM FAccT Conference
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