Welfare and Distributional Impacts of Fair Classification
This work addresses the need for a more explicit consideration of distributive justice in algorithmic fairness, offering a novel perspective for researchers and practitioners in the field.
The paper tackles the problem of interpreting fairness criteria in machine learning by proposing a new framework that converts constrained loss minimization into a social welfare maximization problem, revealing how predictions and fairness constraints shape societal welfare and distribution.
Current methodologies in machine learning analyze the effects of various statistical parity notions of fairness primarily in light of their impacts on predictive accuracy and vendor utility loss. In this paper, we propose a new framework for interpreting the effects of fairness criteria by converting the constrained loss minimization problem into a social welfare maximization problem. This translation moves a classifier and its output into utility space where individuals, groups, and society at-large experience different welfare changes due to classification assignments. Under this characterization, predictions and fairness constraints are seen as shaping societal welfare and distribution and revealing individuals' implied welfare weights in society--weights that may then be interpreted through a fairness lens. The social welfare formulation of the fairness problem brings to the fore concerns of distributive justice that have always had a central albeit more implicit role in standard algorithmic fairness approaches.