一句话：提出通过调试训练数据来缓解机器学习软件因敏感特征导致的不公平性。
随着机器学习软件在高风险任务中的广泛应用，其公平性受到开发者和用户的关注。所谓不公平，是指软件行为受到性别等敏感特征影响，从而产生有偏甚至违法的决策，已成为软件工程社区值得研究的问题。论文围绕这一痛点，从训练数据调试的角度切入，旨在通过发现和修正训练数据中的偏差来提升机器学习软件的公平性。不过，所给摘要仅阐述了问题背景与动机，未具体说明数据调试的技术路线、公平性度量、工具实现或实验评估与结果。
With the widespread application of machine learning (ML) software, especially in high-risk tasks, the concern about their unfairness has been raised towards both developers and users of ML software. The unfairness of ML software indicates the software behavior affected by the sensitive features (e.g., sex), which leads to biased and illegal decisions and has become a worthy problem for the whole software engineering community.
@inproceedings{LiMC0WZX22,
title = {Training data debugging for the fairness of machine learning software},
author = {Yanhui Li and Linghan Meng and Lin Chen and Li Yu and Di Wu and Yuming Zhou and Baowen Xu},
booktitle = {ICSE},
year = {2022},
doi = {10.1145/3510003.3510091}
}