Exploiting reject option in classification for social discrimination control
Exploiting reject option in classification for social discrimination control
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DOI：
10.1016/j.ins.2017.09.064
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发表时间：
2018-01-01
影响因子：
8.1
通讯作者：
Zhang, Xiangliang
中科院分区：
文献类型：
--
作者：
Kamiran, Faisal;Mansha, Sameen;Zhang, Xiangliang
据说，当一个人因属于某些受保护群体（例如女性和少数族裔群体）而做出对其不利的决定时，就会发生社会歧视。历史数据中经常存在这样的歧视性决定。尽管最近在歧视感知数据挖掘方面取得了一些进展，但仍然需要强大且易于使用的歧视控制方法。在本文中，我们利用分类中的拒绝选项，这是一种用于处理标签不确定的实例的通用决策理论框架，用于建模和控制歧视性决策。具体来说，该框架允许对直觉进行正式处理，即接近决策边界的实例更有可能在数据集中受到歧视。基于这个框架，我们提出了三种不同的歧视感知分类解决方案。第一个解决方案在单个或多个概率分类器中调用概率拒绝，而第二个解决方案依赖于分类器集合中的集合拒绝。第三种解决方案将前两种解决方案之一与情境测试相结合，这是法庭上常用的程序。所有解决方案都易于使用，并为决策提供强有力的理由。我们在四个现实世界数据集上广泛评估我们的解决方案，并将其性能与之前提出的歧视感知分类器进行比较。结果证明了我们的解决方案在性能和适用灵活性方面的优越性。特别是，我们的解决方案可以有效消除预测中的非法歧视。 (C) 2017 年，爱思唯尔公司出版
Social discrimination is said to occur when an unfavorable decision for an individual is influenced by her membership to certain protected groups such as females and minority ethnic groups. Such discriminatory decisions often exist in historical data. Despite recent works in discrimination-aware data mining, there remains the need for robust, yet easily usable, methods for discrimination control. In this paper, we utilize reject option in classification, a general decision theoretic framework for handling instances whose labels are uncertain, for modeling and controlling discriminatory decisions. Specifically, this framework permits a formal treatment of the intuition that instances close to the decision boundary are more likely to be discriminated in a dataset. Based on this framework, we present three different solutions for discrimination-aware classification. The first solution invokes probabilistic rejection in single or multiple probabilistic classifiers while the second solution relies upon ensemble rejection in classifier ensembles. The third solution integrates one of the first two solutions with situation testing which is a procedure commonly used in the court of law. All solutions are easy to use and provide strong justifications for the decisions. We evaluate our solutions extensively on four real-world datasets and compare their performances with previously proposed discrimination-aware classifiers. The results demonstrate the superiority of our solutions in terms of both performance and flexibility of applicability. In particular, our solutions are effective at removing illegal discrimination from the predictions. (C) 2017 Published by Elsevier Inc.