Training Data Debugging for the Fairness of Machine Learning Software
Yanhui Li, Linghan Meng, Lin Chen, Li Yu, Di Wu, Yuming Zhou, Baowen Xu
2022年份
49被引次数
14顶会引用
摘要
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.
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- XInsight: eXplainable Data Analysis Through The Lens of CausalityPingchuan Ma, Rui Ding, Shuai Wang, Shi Han 等SIGMOD 2023 · 被引用 20 次
- Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoMLGiang Nguyen, Sumon Biswas, Hridesh RajanFSE 2023 · 被引用 15 次
- Dynamic Data Fault Localization for Deep Neural NetworksYining Yin, Yang Feng, Shihao Weng, Zixi Liu 等FSE 2023 · 被引用 10 次
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