Fairness through Equality of Effort

TL;DR

This paper introduces a new causal fairness concept called equality of effort, which assesses the effort needed for individuals to reach certain outcomes, and develops algorithms to detect and mitigate discrimination based on this measure.

Contribution

It proposes a novel fairness notion, equality of effort, and provides algorithms for discrimination detection and removal based on this concept.

Findings

Algorithms effectively detect discrimination in terms of effort.

Empirical results show the new fairness measure offers different insights than existing notions.

Optimization methods successfully reduce discriminatory effects in data.

Abstract

Fair machine learning is receiving an increasing attention in machine learning fields. Researchers in fair learning have developed correlation or association-based measures such as demographic disparity, mistreatment disparity, calibration, causal-based measures such as total effect, direct and indirect discrimination, and counterfactual fairness, and fairness notions such as equality of opportunity and equal odds that consider both decisions in the training data and decisions made by predictive models. In this paper, we develop a new causal-based fairness notation, called equality of effort. Different from existing fairness notions which mainly focus on discovering the disparity of decisions between two groups of individuals, the proposed equality of effort notation helps answer questions like to what extend a legitimate variable should change to make a particular individual achieve a certain outcome level and addresses the concerns whether the efforts made to achieve the same outcome level for individuals from the protected group and that from the unprotected group are different. We develop algorithms for determining whether an individual or a group of individuals is discriminated in terms of equality of effort. We also develop an optimization-based method for removing discriminatory effects from the data if discrimination is detected. We conduct empirical evaluations to compare the equality of effort and existing fairness notion and show the effectiveness of our proposed algorithms.