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Analysis for data preprocessing to prevent direct discrimination in data mining

机译：数据预处理分析，以防止数据挖掘中的直接歧视

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摘要

Data mining is a technology using which we can extract useful information from data. There are two major issues in data mining first is privacy violation and and second is discrimination. Discrimination is the unfair treatment with respect to the features that should not be considered while decision making. With respect to human, it is when people are given unfair treatment on the basis of their sensitive features like gender, race, religion etc. Discrimination can be of two types direct discrimination and indirect discrimination. Direct discrimination consists of training rules based on sensitive attributes like religion, race, community etc. Indirect discrimination is a discrimination which occurs when the decisions are taken on non-sensitive attributes but these attributes are closely related to direct discriminatory attributes. Automated decision making systems uses data mining techniques to train the system for decision making. Data form the previous work is used for the rule generation to train the system. At first sight, we can say that automating decisions systems are fair in decision making, but if the training data sets are itself discriminatory then the the system cannot be free from discrimination. To remove such discrimination we have discrimination discovery and prevention techniques in data mining. This paper mainly focuses direct discrimination removal from the data.
机译：数据挖掘是一种可以从数据中提取有用信息的技术。数据挖掘中有两个主要问题，首先是侵犯隐私，其次是歧视。对于在决策过程中不应考虑的功能，歧视是一种不公平的对待。对于人而言，正是基于性别，种族，宗教等敏感特征，人们受到不公平对待。歧视可以分为直接歧视和间接歧视两种类型。直接歧视包括基于诸如宗教，种族，社区等敏感属性的训练规则。间接歧视是在对非敏感属性做出决定时发生的歧视，但这些属性与直接歧视属性密切相关。自动化决策系统使用数据挖掘技术来训练系统进行决策。先前工作的数据用于规则生成以训练系统。乍看之下，我们可以说自动化决策系统在决策过程中是公平的，但是如果训练数据集本身具有歧视性，那么该系统就无法摆脱歧视。为了消除这种歧视，我们在数据挖掘中采用了歧视发现和预防技术。本文主要着重于从数据中去除直接歧视。

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