Training Data Debugging for the Fairness of Machine Learning Software

Yanhui Li, Linghan Meng, Lin Chen, Li Yu, Di Wu, Yuming Zhou, Baowen Xu

Abstract

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.

BibTeX

@inproceedings{Li-al:ICSE22,
  author    = {Yanhui Li and
               Linghan Meng and
               Lin Chen and
               Li Yu and
               Di Wu and
               Yuming Zhou and
               Baowen Xu},
  title     = {Training Data Debugging for the Fairness of Machine Learning Software},
  booktitle = {ICSE},
  pages     = {2215--2227},
  publisher = {{ACM}},
  year      = {2022},
}

Training Data Debugging for the Fairness of Machine Learning Software

Abstract

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