论文标题
结合预处理算法能否导致更好的机器学习公平？
Can Ensembling Pre-processing Algorithms Lead to Better Machine Learning Fairness?
论文作者
论文摘要
随着机器学习（ML）系统在更关键的领域被采用，解决这些系统中可能发生的偏见变得越来越重要。可以使用几种公平的预处理算法来减轻模型培训期间的隐性偏见。这些算法采用了不同的公平概念，通常会导致冲突的策略，而公平与准确性之间的折衷方案。在这项工作中，我们评估了三种普遍的预处理算法，并研究了将所有算法组合为更强大的预处理集合的潜力。我们报告的经验教训可以帮助从业者更好地为其模型选择公平算法。
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.
