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
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information
Taught by
ACM FAccT Conference
Related Courses
-
Practical Methodology and Ethics in AI
-
Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness
-
Bias Preservation in Machine Learning - The Legality of Fairness Metrics Under EU Non-Discrimination Law
-
I Agree with the Decision, but They Didn't Deserve This - Future Developers' Perception of Fairness
-
Avoiding Disparity Amplification under Different Worldviews
-
Bias Preservation in Machine Learning - The Legality of Fairness Metrics Under EU Non-Discrimination Law
