Unpacking ML Fairness
Zachary Lipton explores the limitations of ML fairness work, highlighting the need to consider variables, data sources, and the potential trace of discriminatory policies. He challenges the notion of a generic approach to fairness and emphasizes the importance of understanding the decision-making process and its potential impact on different groups of people.In this clip
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The Gradient
Zachary Lipton: Where Machine Learning Falls Short
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