The Independence of the Fairness-aware Classifiers
The Independence of the Fairness-aware Classifiers
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公平感知分类器的独立性
DOI：
10.1109/icdmw.2013.133
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
佐久間淳
中科院分区：
文献类型：
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作者：
神嶌敏弘;赤穂昭太郎;麻生英樹;佐久間淳
由于数据挖掘技术的传播，这些技术正被用于严重影响个人生活的确定。例如，信用评分通常基于过去的信用数据记录以及统计预测技术来确定。毫无疑问，这种决定必须是非歧视性的，并且在敏感特征上是公平的，例如种族、性别、宗教等。公平意识分类器的目标是在对数据进行分类的同时考虑公平、歧视、中立和/或独立性的潜在问题。在本文中，在回顾公平意识的分类方法，我们专注于这样一种方法，考尔德斯和Verwer的两个朴素贝叶斯方法。该方法已被证明上级其他分类器的公平性，这是形式化的类和敏感特征之间的统计独立性。然而，这种优越性的原因尚不清楚，因为它采用了一种启发式的后处理技术，而不是一个明确的形式化模型。我们澄清的原因，通过比较这种方法与替代朴素贝叶斯分类器，这是修改的建模技术称为“假设公平因子分解。“这项研究揭示了双朴素贝叶斯方法的理论背景及其与其他方法的联系。基于这些研究结果，我们开发了另一种朴素贝叶斯方法与“实际公平因子分解技术”，并实证表明，这种新方法可以实现同等水平的公平性的两个朴素贝叶斯分类器。
Due to the spread of data mining technologies, such technologies are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair in sensitive features, such as race, gender, religion, and so on. The goal of fairness-aware classifiers is to classify data while taking into account the potential issues of fairness, discrimination, neutrality, and/or independence. In this paper, after reviewing fairness-aware classification methods, we focus on one such method, Calders and Verwer's two-naive-Bayes method. This method has been shown superior to the other classifiers in terms of fairness, which is formalized as the statistical independence between a class and a sensitive feature. However, the cause of the superiority is unclear, because it utilizes a somewhat heuristic post-processing technique rather than an explicitly formalized model. We clarify the cause by comparing this method with an alternative naive Bayes classifier, which is modified by a modeling technique called "hypothetical fair-factorization." This investigation reveals the theoretical background of the two-naive-Bayes method and its connections with other methods. Based on these findings, we develop another naive Bayes method with an "actual fair-factorization technique" and empirically show that this new method can achieve an equal level of fairness as that of the two-naive-Bayes classifier.