论文标题
公平的无穷小折刀：减轻偏见的培训数据点的影响而无需改装
Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting
论文作者
论文摘要
在相应的决策应用中，减轻机器学习模型中不必要的偏见，这些偏见会给诸如种族和性别等敏感属性所描述的群体成员带来系统的劣势，这是争取公平的关键干预措施。专注于人口统计学和机会平等，在本文中，我们提出了一种算法，通过简单地删除精心选择的培训数据点来改善预训练的分类器的公平性。我们根据使用基于无限折刀的方法计算出的实例对它们对公平度量的影响进行选择。训练点的降低原则上是完成的，但实际上不需要重新装修模型。至关重要的是，我们发现这种干预并不能大大降低模型的预测性能，但会大大改善公平度量。通过仔细的实验，我们评估了拟议方法对各种任务的有效性，并发现它在现有替代方案上持续改善。
In consequential decision-making applications, mitigating unwanted biases in machine learning models that yield systematic disadvantage to members of groups delineated by sensitive attributes such as race and gender is one key intervention to strive for equity. Focusing on demographic parity and equality of opportunity, in this paper we propose an algorithm that improves the fairness of a pre-trained classifier by simply dropping carefully selected training data points. We select instances based on their influence on the fairness metric of interest, computed using an infinitesimal jackknife-based approach. The dropping of training points is done in principle, but in practice does not require the model to be refit. Crucially, we find that such an intervention does not substantially reduce the predictive performance of the model but drastically improves the fairness metric. Through careful experiments, we evaluate the effectiveness of the proposed approach on diverse tasks and find that it consistently improves upon existing alternatives.
