Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information
Offered By: Association for Computing Machinery (ACM) via YouTube
Course Description
Overview
Explore a 20-minute conference talk from the FAccT 2021 virtual event that delves into the challenges of evaluating fairness in machine learning models when faced with uncertain and incomplete information. Learn how researchers P. Awasthi, A. Beutel, M. Kleindessner, J. Morgenstern, and X. Wang address this critical issue in the field of AI ethics and fairness. Gain insights into novel approaches for assessing model fairness under constrained data scenarios and understand the implications for developing more equitable AI systems.
Syllabus
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information
Taught by
ACM FAccT Conference
Related Courses
Introduction to Artificial IntelligenceStanford University via Udacity Natural Language Processing
Columbia University via Coursera Probabilistic Graphical Models 1: Representation
Stanford University via Coursera Computer Vision: The Fundamentals
University of California, Berkeley via Coursera Learning from Data (Introductory Machine Learning course)
California Institute of Technology via Independent