Handling Conditional Discrimination
Handling Conditional Discrimination
复制标题
处理有条件的歧视
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
T. Calders
中科院分区：
文献类型：
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作者：
Indrė Žliobaitė;F. Kamiran;T. Calders
用于监督学习的历史数据可能包含歧视。我们研究如何在此类数据上培训分类器，以便相对于给定的敏感属性，例如性别。处理此问题的现有技术旨在消除所有歧视，而没有考虑到部分歧视可以通过其他属性（例如教育水平）来解释。在这种情况下，我们介绍和分析了分类器设计中条件非歧视的问题。我们表明，整个敏感群体的决策中的某些差异可以解释，因此可以容忍。我们观察到，在这种情况下，现有的歧视意识技术将引入反向歧视，这也是不受欢迎的。因此，当其中一个属性被认为是解释性时，我们开发了处理条件歧视的本地技术。实验评估表明，新的本地技术完全消除了不良歧视，只要可以解释，就可以在决策中差异。
Historical data used for supervised learning may contain discrimination. We study how to train classifiers on such data, so that they are discrimination free with respect to a given sensitive attribute, e.g., gender. Existing techniques that deal with this problem aim at removing all discrimination and do not take into account that part of the discrimination may be explainable by other attributes, such as, e.g., education level. In this context, we introduce and analyze the issue of conditional non-discrimination in classifier design. We show that some of the differences in decisions across the sensitive groups can be explainable and hence tolerable. We observe that in such cases, the existing discrimination aware techniques will introduce a reverse discrimination, which is undesirable as well. Therefore, we develop local techniques for handling conditional discrimination when one of the attributes is considered to be explanatory. Experimental evaluation demonstrates that the new local techniques remove exactly the bad discrimination, allowing differences in decisions as long as they are explainable.