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Sara Hooker - The Hardware Lottery, Sparsity and Fairness

Machine Learning Street Talk (MLST)

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Navigating the Complexities of Machine Learning Interpretability

This chapter explores the complexities of machine learning, especially how representation capacity affects model performance on underrepresented groups. It critiques common interpretability methods and discusses the promising potential of interpretability in enhancing our understanding of model behavior and decision-making.

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