Toward Unifying Group Fairness Evaluation from a Sparsity Perspective
Zhecheng Sheng, Jiawei Zhang, Enmao Diao
TL;DR
This paper introduces a unified sparsity-based framework for evaluating algorithmic fairness, connecting various fairness criteria and demonstrating broad applicability across different machine learning tasks.
Contribution
It proposes a novel sparsity perspective to unify and evaluate diverse fairness measures, enhancing the generalizability of fairness assessment methods.
Findings
The framework aligns with existing fairness criteria.
Extensive experiments validate the framework's effectiveness.
Applicable across multiple datasets and bias mitigation techniques.
Abstract
Ensuring algorithmic fairness remains a significant challenge in machine learning, particularly as models are increasingly applied across diverse domains. While numerous fairness criteria exist, they often lack generalizability across different machine learning problems. This paper examines the connections and differences among various sparsity measures in promoting fairness and proposes a unified sparsity-based framework for evaluating algorithmic fairness. The framework aligns with existing fairness criteria and demonstrates broad applicability to a wide range of machine learning tasks. We demonstrate the effectiveness of the proposed framework as an evaluation metric through extensive experiments on a variety of datasets and bias mitigation methods. This work provides a novel perspective to algorithmic fairness by framing it through the lens of sparsity and social equity, offering…
Peer Reviews
Decision·Submitted to ICLR 2026
1. Using sparsity to connect different group-fairness metrics is an interesting idea that is rarely explored in existing fairness literature. 2. The theoretical section is well-written and mathematically grounded.
1. Limited novelty beyond reinterpretation. While the sparsity–fairness connection is elegant, the framework largely rephrases existing fairness measures in new mathematical form. The advantage of the proposed framework is more evident in intersectional fairness settings. Nonetheless, beyond these intersectional cases, the contribution remains primarily interpretive rather than methodological. There is little evidence that the proposed sparsity-based metrics yield different conclusions compared
+ Since sparsity measures have been used in studies on social inequality/fairness, the use of these measures in studying algorithmic fairness seems to be warranted. + Interesting theoretical results characterizing the properties of sparsity measures.
- Paper did not clearly state what benefits they found in their examination. Experimental results on the 6 datasets seemed to indicate both baseline fairness metrics and sparsity-based measures had similar tradeoff curves regarding model performance and fairness. - While there were results on a single dataset suggesting that sparsity-based metrics were “better” at handling group heterogeneity (sec 5.3). I felt the experiments were too limiting to draw strong conclusions for or against the us
The paper is well-motivated, addressing an important topic in fairness, the need for appropriate metrics that effectively capture the disparity we aim to measure. It is well-written and features extensive experiments that consider multiple datasets and bias mitigation techniques. The motivation for using sparsity-based metrics in the context of intersectional fairness is compelling and well-justified, and the results of that section (5.3) are convincing.
The actual contribution of the proposed metric should be better highlighted from the start. Specifically, from what I understand from the subsequent sections, the main advantages are that the metric can be consistently applied to both binary and multi-class settings, and that it captures disparities across the entire group distribution more effectively than MPD in multi-class scenarios. However, the abstract and introduction make broader claims such as “generalizability across different machine
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Taxonomy
TopicsEthics and Social Impacts of AI · Mobile Crowdsensing and Crowdsourcing · Explainable Artificial Intelligence (XAI)