Model-based and actual independence for fairness-aware classification
Model-based and actual independence for fairness-aware classification
复制标题
DOI：
10.1007/s10618-017-0534-x
复制
发表时间：
2018-01-01
影响因子：
4.8
通讯作者：
Sakuma, Jun
中科院分区：
文献类型：
--
作者：
Kamishima, Toshihiro;Akaho, Shotaro;Sakuma, Jun
公平意识分类的目标是对数据进行分类，同时考虑到可能存在的公平、歧视、中立性和/或独立性问题。例如，在将数据挖掘技术应用于大学招生时，录取标准在性别或种族等敏感特征方面必须是非歧视性和公平的。在这种情况下，这种公平性可以被形式化为分类结果和敏感特征之间的统计独立性。本文的主要目的是对这种形式公平性进行分析，以便在公平性和预测精度之间实现更好的折衷，这对公平性感知分类器在实际应用中的应用具有重要意义。我们重点研究了一种公平感知的分类器--Calders和Verwers Two-naive-Bayes(CV2NB)方法，该方法已被证明在公平性方面优于其他分类器。我们假设，这种优势是由于独立类型的不同造成的。也就是说，因为CV2NB实现了实际的独立性，而不是像其他分类器那样满足基于模型的独立性，所以它可以解释模型偏差和确定性决策规则。我们通过修改两个公平感知分类器--偏见消除方法和基于拒绝选项的分类(ROC)方法来实证验证这一假设，以满足实际的独立性。这两个修改后的方法的公平性得到了显著提高，表明了保持实际独立性的重要性，而不是基于模型的独立性。我们还扩展了ROC方法中采用的一种方法，使其适用于除具有生成模型的分类器之外的其他分类器，如支持向量机。
The goal of fairness-aware classification is to categorize data while taking into account potential issues of fairness, discrimination, neutrality, and/or independence. For example, when applying data mining technologies to university admissions, admission criteria must be non-discriminatory and fair with regard to sensitive features, such as gender or race. In this context, such fairness can be formalized as statistical independence between classification results and sensitive features. The main purpose of this paper is to analyze this formal fairness in order to achieve better trade-offs between fairness and prediction accuracy, which is important for applying fairness-aware classifiers in practical use. We focus on a fairness-aware classifier, Calders and Verwer's two-naive-Bayes (CV2NB) method, which has been shown to be superior to other classifiers in terms of fairness. We hypothesize that this superiority is due to the difference in types of independence. That is, because CV2NB achieves actual independence, rather than satisfying model-based independence like the other classifiers, it can account for model bias and a deterministic decision rule. We empirically validate this hypothesis by modifying two fairness-aware classifiers, a prejudice remover method and a reject option-based classification (ROC) method, so as to satisfy actual independence. The fairness of these two modified methods was drastically improved, showing the importance of maintaining actual independence, rather than model-based independence. We additionally extend an approach adopted in the ROC method so as to make it applicable to classifiers other than those with generative models, such as SVMs.