Advancing Algorithmic Fairness: A Statistical Learning Approach with Fairness Constraints

Oregon Convention Center 

Statistical machine learning algorithms, crucial in sectors like hiring, finance, and healthcare, risk reinforcing societal biases based on gender, race, religion, among others. To combat this, it's vital to design models adhering to fairness norms. This involves embedding fairness constraints such as 'equal opportunity' [Hardt et al., 2016], ensuring uniform true positive rates across groups, and 'path-specific counterfactual fairness' [Nabi and Shpitser, 2018, Nabi et al., 2019], which restricts the effect of the sensitive feature on the outcome along certain user-specified mediating pathways. Without favoring a specific fairness criterion, we propose a general framework for deriving optimal prediction functions under various constraints. It conceptualizes the learning problem as estimating a constrained functional parameter within a comprehensive statistical model, using a Lagrange-type penalty. Key contributions of our work include a flexible framework for solving constrained optimization problems, closed-form solutions for specific fairness constraints, and an algorithm-neutral approach to fair learning.