Fairness through Equality of Effort

2019/11/11 by Wen Huang, Yongkai Wu, Huang, Wen +5 · 1 citation
Computer Science · Mathematics · Social Sciences · #Advanced Causal Inference Techniques #Artificial Intelligence (cs.AI) #Computers and Society (cs.CY) #Ethics and Social Impacts of AI #FOS: Computer and information sciences #Qualitative Comparative Analysis Research #cs.AI #cs.CY

paper · pdf · doi:10.48550/arxiv.1911.08292

arxiv created 2019/11/11 · openalex publication_date 2019/11/11 · arxiv updated 2019/11/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• The central role of the propensity score in observational studies for causal effects
• Equality of Opportunity in Supervised Learning
• Counterfactual Fairness
• On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning
• PC-Fairness: A Unified Framework for Measuring Causality-based Fairness

Cited by

• Effort-aware Fairness: Incorporating a Philosophy-informed, Human-centered Notion of Effort into Algorithmic Fairness Metrics

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