Hort, Max;
Zhang, Jie M;
Sarro, Federica;
Harman, Mark;
(2024)
Search-based Automatic Repair for Fairness and Accuracy in Decision-making Software.
Empirical Software Engineering
, 29
, Article 36. 10.1007/s10664-023-10419-3.
(In press).
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Abstract
Decision-making software mainly based on Machine Learning (ML) may contain fairness issues (e.g., providing favourable treatment to certain people rather than others based on sensitive attributes such as gender or race). Various mitigation methods have been proposed to automatically repair fairness issues to achieve fairer ML software and help software engineers to create responsible software. However, existing bias mitigation methods trade accuracy for fairness (i.e., trade a reduction in accuracy for better fairness). In this paper, we present a novel search-based method for repairing ML-based decision making software to simultaneously increase both its fairness and accuracy. As far as we know, this is the first bias mitigation approach based on multi-objective search that aims to repair fairness issues without trading accuracy for binary classification methods. We apply our approach to two widely studied ML models in the software fairness literature (i.e., Logistic Regression and Decision Trees), and compare it with seven publicly available state-of-the-art bias mitigation methods by using three different fairness measurements. The results show that our approach successfully increases both accuracy and fairness for 61% of the cases studied, while the state-of-the-art always decrease accuracy when attempting to reduce bias. With our proposed approach, software engineers that previously were concerned with accuracy losses when considering fairness, are now enabled to improve the fairness of binary classification models without sacrificing accuracy.
| Type: | Article |
|---|---|
| Title: | Search-based Automatic Repair for Fairness and Accuracy in Decision-making Software |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s10664-023-10419-3 |
| Publisher version: | https://doi.org/10.1007/s10664-023-10419-3 |
| Language: | English |
| Additional information: | © The Author(s), 2024. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Software fairness, Bias mitigation, Classification, Multi-objective optimization |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10185565 |
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