Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information

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.

Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information