Sharad Goel

In this enlightening discussion, Sharad Goel, a Harvard public policy professor and expert on fairness in machine learning, shares his insights on causal conceptions of fairness. He unpacks the limitations of traditional fairness definitions, emphasizing the role of causality in understanding bias in algorithmic decision-making. Goel explores the tension in policy-making between procedural and outcome fairness, revealing how rigid adherence to causal fairness can lead to suboptimal results. He also delves into surprising findings regarding Pareto dominance and its implications for equitable policies.