Pre-Processing Approach for Discrimination Prevention in Data Mining.

Data mining is an important technology for extracting useful knowledge hidden in large collections of data. In data mining, discrimination is a very important issue when considering the legal and ethical aspects of data mining. It is more than observable that the majority people do not want to be discriminated because of their gender, nationality, religion, age and so on. Especially when these type of attributes are used for decision making purpose such as giving them a job, loan. Insurance etc.. Discrimination can be either direct or indirect. Direct discrimination occurs when decisions are made based on sensitive attributes. Indirect discrimination occurs when decisions are made based on non-sensitive attributes which are strongly correlated with biased sensitive ones. So we introduce an antidiscrimination techniques which including discrimination discovery and prevention. In the discrimination prevention method, we introduce a group of pre-processing discrimination prevention methods and specify the different features of each approach and how these approaches deal with direct or indirect discrimination. We discuss how to clean training data sets and outsourced data sets in such a way that direct and/or indirect discriminatory decision rules are converted to nondiscriminatory classification rules. Some metrics are used to evaluate the performance of those approaches is also given