一句话：提出预处理方法 CoT，利用 Phi 系数与多目标优化调优数据相关性，在缓解 ML 偏差的同时改善性能。
传统公平性研究多强调伦理与社会责任，忽视了公平性本质上是敏感用户群体间性能差异导致的软件质量问题。现有偏差缓解方法存在两难：预处理方法适用广但效果常不如后处理。为此，作者提出相关性调优（CoT），一种通过调整数据相关性来缓解偏差的预处理方法。CoT 引入 Phi 系数量化敏感属性与标签的相关性，并用多目标优化处理代理偏差。评估表明，CoT 将弱势群体真正例率平均提高 17.5%，将 SPD、AOD、EOD 三项偏差指标平均降低 50% 以上；在单/多敏感属性场景下分别优于最先进方法 3 和 10 个百分点。
Traditional software fairness research typically emphasizes ethical and social imperatives, neglecting that fairness fundamentally represents a core software quality issue arising directly from performance disparities across sensitive user groups. Recognizing fairness explicitly as a software quality dimension yields practical benefits beyond ethical considerations, notably improved predictive performance for unprivileged groups, enhanced out-of-distribution generalization, and increased geographic transferability in real-world deployments. Nevertheless, existing bias mitigation methods face a critical dilemma: while pre-processing methods offer broad applicability across model types, they generally fall short in effectiveness compared to post-processing techniques. To overcome this challenge, we propose Correlation Tuning (CoT), a novel pre-processing approach designed to mitigate bias by adjusting data correlations. Specifically, CoT introduces the Phi-coefficient, an intuitive correlation measure, to systematically quantify correlation between sensitive attributes and labels, and employs multi-objective optimization to address the proxy biases. Extensive evaluations demonstrate that CoT increases the true positive rate of unprivileged groups by an average of 17.5% and reduces three key bias metrics, including statistical parity difference (SPD), average odds difference (AOD), and equal opportunity difference (EOD), by more than 50% on average. CoT outperforms state-of-the-art methods by three and ten percentage points in single attribute and multiple attributes scenarios, respectively. We will publicly release our experimental results and source code to facilitate future research.
@inproceedings{abs-2512-21348,
title = {Fairness Is Not Just Ethical: Performance Trade-Off via Data Correlation Tuning to Mitigate Bias in ML Software},
author = {Ying Xiao and Shangwen Wang and Sicen Liu and Dingyuan Xue and Xian Zhan and Yepang Liu and Jie Zhang},
booktitle = {ICSE},
year = {2026},
doi = {10.1145/3744916.3787794}
}