# Welfare and Distributional Impacts of Fair Classification

arXiv: https://arxiv.org/abs/1807.01134 · HTML: https://mlsanity.com/paper/1807.01134/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2018.

## Built on by (most cited first, 5 of 5)

- [Enforcing Delayed-Impact Fairness Guarantees](https://mlsanity.com/paper/2208.11744/) (2022)
- [Are There Exceptions to Goodhart's Law? On the Moral Justification of Fairness-Aware Machine Learning](https://mlsanity.com/paper/2202.08536/) (2022)
- [(Machine) Learning What Policies Value](https://mlsanity.com/paper/2206.00727/) (2022)
- [Fairness vs Performance: Characterizing the Pareto Frontier of Algorithmic Decision Systems](https://mlsanity.com/paper/2605.10604/) (2026)
- [Assessing Group Fairness with Social Welfare Optimization](https://mlsanity.com/paper/2405.11421/) (2024)

## Abstract (arXiv)

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.