Join us for a deep dive into the insights of Fairness and Machine Learning by Solon Barocas, Moritz Hardt, and Arvind Narayanan. We’ll explore how fairness is defined and measured, the legal and societal context that shapes it, and the power of causality and counterfactual reasoning in identifying discrimination. From testing bias in practice to understanding “merit and desert,” this episode unpacks the limitations and opportunities of automated decision-making—and why machine learning should never be viewed as a simple replacement for human judgment.

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