Considerations on Fairness-aware Data Mining
Considerations on Fairness-aware Data Mining
复制标题
公平感知数据挖掘的思考
DOI：
10.1109/icdmw.2012.101
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
J. Sakuma
中科院分区：
文献类型：
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作者：
T. Kamishima;S. Akaho;H. Asoh;J. Sakuma
随着数据挖掘技术的普及和社会数据的积累，这些技术和数据正被用于严重影响个人生活的决定。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。毋庸置疑，这样的决定必须是非歧视性的和公平的敏感特征，如种族，性别，宗教，最近几个研究人员已经开始开发公平意识或歧视意识的数据挖掘技术，考虑到社会公平，歧视和中立的问题。在本文中，在演示了这些技术的应用，我们探讨了公平性的正式概念和技术处理公平性数据挖掘。然后，我们根据统计独立性提供这些概念的综合视图。最后，我们讨论了公平感知数据挖掘和其他研究主题，如隐私保护数据挖掘或因果推理之间的关系。
With the spread of data mining technologies and the accumulation of social data, such technologies and data 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 regarding sensitive features such as race, gender, religion, and so on. Several researchers have recently begun to develop fairness-aware or discrimination-aware data mining techniques that take into account issues of social fairness, discrimination, and neutrality. In this paper, after demonstrating the applications of these techniques, we explore the formal concepts of fairness and techniques for handling fairness in data mining. We then provide an integrated view of these concepts based on statistical independence. Finally, we discuss the relations between fairness-aware data mining and other research topics, such as privacy-preserving data mining or causal inference.