Bias in Compact Models
Sara discusses how compressing models can amplify bias, especially when protected attributes are underrepresented in the data. The study explores how model sparsity impacts fairness and the treatment of protected attributes in machine learning models.In this clip
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Machine Learning Street Talk (MLST)
Sara Hooker - The Hardware Lottery, Sparsity and Fairness
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