A refined reweighing technique for nondiscriminatory classification

Discrimination-aware classification methods remedy socioeconomic disparities exacerbated by machine learning systems. In this paper, we propose a novel data pre-processing technique that assigns weights to training instances in order to reduce discrimination without changing any of the inputs or lab...

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Bibliographic Details
Published in:PloS one 2024-08, Vol.19 (8), p.e0308661
Main Authors: Liang, Yuefeng, Hsieh, Cho-Jui, Lee, Thomas C M
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Artificial intelligence
Classification
Computer and Information Sciences
Discrimination
Humans
Linear programming
Machine Learning
Methods
Optimization
Parity
Physical Sciences
Probability
Probability distribution
Research and Analysis Methods
Simulation
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Summary:Discrimination-aware classification methods remedy socioeconomic disparities exacerbated by machine learning systems. In this paper, we propose a novel data pre-processing technique that assigns weights to training instances in order to reduce discrimination without changing any of the inputs or labels. While the existing reweighing approach only looks into sensitive attributes, we refine the weights by utilizing both sensitive and insensitive ones. We formulate our weight assignment as a linear programming problem. The weights can be directly used in any classification model into which they are incorporated. We demonstrate three advantages of our approach on synthetic and benchmark datasets. First, discrimination reduction comes at a small cost in accuracy. Second, our method is more scalable than most other pre-processing methods. Third, the trade-off between fairness and accuracy can be explicitly monitored by model users. Code is available at https://github.com/frnliang/refined_reweighing.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0308661