论文标题
我们在机器学习中有多公平？评估决策树中公平的界限
How fair can we go in machine learning? Assessing the boundaries of fairness in decision trees
论文作者
论文摘要
公平的机器学习工作一直集中在解决某些群体歧视的公平算法上的发展。然而，这些公平感知的方法旨在为问题获得独特的解决方案，从而使人们对缓解偏见的统计限制的理解不足。我们提出了第一种允许在多目标框架内探索这些限制的方法，该框架旨在优化任何准确性和公平性，并提供具有最佳可行解决方案的帕累托阵线。在这项工作中，我们将研究重点放在决策树分类器上，因为它们在机器学习中被广泛接受，易于解释，并且可以自然处理非数字信息。我们从实验上得出结论，我们的方法可以通过更公平的分类错误来优化决策树模型。我们认为，我们的贡献将有助于社会技术系统的利益相关者评估他们可以公平和准确的距离，从而为支持使用机器学习的增强决策做出服务。
Fair machine learning works have been focusing on the development of equitable algorithms that address discrimination of certain groups. Yet, many of these fairness-aware approaches aim to obtain a unique solution to the problem, which leads to a poor understanding of the statistical limits of bias mitigation interventions. We present the first methodology that allows to explore those limits within a multi-objective framework that seeks to optimize any measure of accuracy and fairness and provides a Pareto front with the best feasible solutions. In this work, we focus our study on decision tree classifiers since they are widely accepted in machine learning, are easy to interpret and can deal with non-numerical information naturally. We conclude experimentally that our method can optimize decision tree models by being fairer with a small cost of the classification error. We believe that our contribution will help stakeholders of sociotechnical systems to assess how far they can go being fair and accurate, thus serving in the support of enhanced decision making where machine learning is used.
