Data mining is a very important and useful technique to extract knowledge from raw data. However, there is a challenge faced by data mining researchers, in the form of potential discrimination. Discrimination means giving unfair treatment to a person just because one belongs to a minority group, without considering one's individual merit or qualification. The results extracted using data mining techniques may lead to discrimination, if a biased historical/training dataset is used. It is very important to prevent data mining technique from becoming a source of discrimination. A detailed survey of discrimination discovery methods and discrimination prevention methods is presented in this paper. This paper also presents the list of datasets used for experiments in different discrimination-aware data mining (DADM) approaches. Some ideas for future research work that may help in preventing discrimination are also discussed.
Recently, it is observed that data mining technique may come across two problems-potential discrimination and potential privacy violation. Discrimination occurs as a result of use of discriminatory datasets for data mining tasks. Privacy violation occurs if a person's sensitive information is displayed to an unauthorized entity as a result of data mining tasks. Use of privacy preserving techniques to make data privacy protected can affect the amount of discrimination caused. It is important to study the relation of privacy and discrimination in the context of data mining. In this paper, we are trying to propose a method in which privacy preserving technique can be used to prevent discrimination and we can make the original data both privacy protected and discrimination-free.
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