Welfare and Distributional Impacts of Fair Classification

Abstract

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.

arXiv comment5 pages, FATML 2018

References in corpus (2)

• Equality of Opportunity in Supervised Learning
• Penalizing Unfairness in Binary Classification

Cited by in corpus (6)

• Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions
• Measuring justice in machine learning
• To Split or Not to Split: The Impact of Disparate Treatment in Classification
• Dynamic Modeling and Equilibria in Fair Decision Making
• Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning
• Fairness for Whom? Critically reframing fairness with Nash Welfare Product