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Explore the multifaceted concept of multiplicity in machine learning through this 59-minute conference talk presented by researchers from leading institutions including McGill University, MIT, Harvard University, and Mila. Delve into how multiplicity manifests across different aspects of ML systems, from model development to deployment, as experts from academia and industry discuss the various dimensions and implications of this fundamental concept. Examine theoretical foundations and practical considerations surrounding multiplicity in machine learning workflows, drawing insights from interdisciplinary perspectives spanning computer science, statistics, and philosophy. Gain understanding of how multiplicity affects model interpretability, fairness, and robustness while learning about current research directions and methodological approaches to address multiplicity-related challenges in modern ML applications.
