Can Ensembling Pre-processing Algorithms Lead to Better Machine Learning Fairness?

• URL: http://arxiv.org/abs/2212.02614v1
• Date: Mon, 5 Dec 2022 21:54:29 GMT
• Title: Can Ensembling Pre-processing Algorithms Lead to Better Machine Learning Fairness?
• Authors: Khaled Badran, Pierre-Olivier C\^ot\'e, Amanda Kolopanis, Rached Bouchoucha, Antonio Collante, Diego Elias Costa, Emad Shihab, Foutse Khomh
• Abstract summary: Several fairness pre-processing algorithms are available to alleviate implicit biases during model training. These algorithms employ different concepts of fairness, often leading to conflicting strategies with consequential trade-offs between fairness and accuracy. We evaluate three popular fairness pre-processing algorithms and investigate the potential for combining all algorithms into a more robust pre-processing ensemble.
• Score: 8.679212948810916
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As machine learning (ML) systems get adopted in more critical areas, it has become increasingly crucial to address the bias that could occur in these systems. Several fairness pre-processing algorithms are available to alleviate implicit biases during model training. These algorithms employ different concepts of fairness, often leading to conflicting strategies with consequential trade-offs between fairness and accuracy. In this work, we evaluate three popular fairness pre-processing algorithms and investigate the potential for combining all algorithms into a more robust pre-processing ensemble. We report on lessons learned that can help practitioners better select fairness algorithms for their models.

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