A Multi-Objective Framework for Balancing Fairness and Accuracy in Debiasing Machine Learning Models
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make-06-00105-v2.pdf
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3.48 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Nagpal, Rashmi
Khan, Ariba
Borkar, Mihir
Gupta, Amar
Date Issued
September 20, 2024
Journal
machine learning & knowledge extraction
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Nagpal, R.; Khan, A.; Borkar, M.; Gupta, A. A Multi-Objective Framework for Balancing Fairness and Accuracy in Debiasing Machine Learning Models. Mach. Learn. Knowl. Extr. 2024, 6, 2130-2148.
Version
Final published version
Abstract
Machine learning algorithms significantly impact decision-making in high-stakes domains, necessitating a balance between fairness and accuracy. This study introduces an in-processing, multi-objective framework that leverages the Reject Option Classification (ROC) algorithm to simultaneously optimize fairness and accuracy while safeguarding protected attributes such as age and gender. Our approach seeks a multi-objective optimization solution that balances accuracy, group fairness loss, and individual fairness loss. The framework integrates fairness objectives without relying on a weighted summation method, instead focusing on directly optimizing the trade-offs. Empirical evaluations on publicly available datasets, including German Credit, Adult Income, and COMPAS, reveal several significant findings: the ROC-based approach demonstrates superior performance, achieving an accuracy of 94.29%, an individual fairness loss of 0.04, and a group fairness loss of 0.06 on the German Credit dataset. These results underscore the effectiveness of our framework, particularly the ROC component, in enhancing both the fairness and performance of machine learning models.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3390/make6030105
