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Sara Hooker - The Hardware Lottery, Sparsity and Fairness

Machine Learning Street Talk (MLST)

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Navigating AI Bias and Model Training

This chapter explores the intricacies of training AI models, emphasizing Bayesian methods and the significance of addressing data bias, particularly in underrepresented groups. The discussion includes strategies for effective optimization and the potential for automating the identification of protected attributes to enhance fairness in machine learning.

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