Explore a conference talk that delves into the intersection of machine learning and mechanism design to address algorithmic fairness. Discover how researchers J. Finocchiaro, R. Maio, F. Monachou, G. Patro, M. Raghavan, A. Stoica, and S. Tsirtsis present their findings on bridging these two fields to create more equitable algorithmic systems. Learn about the latest developments in this crucial area of study, presented at the FAccT 2021 virtual conference. Gain insights into the challenges and potential solutions for implementing fairness in machine learning algorithms and mechanism design. This 19-minute presentation, part of the Research Track, offers a concise yet comprehensive overview of the topic, making it an essential watch for those interested in the ethical implications of AI and machine learning.
Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness
Taught by
ACM FAccT Conference
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