A proposal for direct and indirect Discrimination prevention

The wide deployment of information systems based on data mining technology in decision making, the issue of antidiscrimination in data mining did not receive much attention until 2008. Some proposals are oriented to the discovery and measure of discrimination. Others deal with the prevention of discrimination. The approach is based on mining classification rules (the inductive part) and reasoning on them (the deductive part) on the basis of quantitative measures of discrimination that formalize legal definitions of discrimination. Discrimination prevention, the other major antidiscrimination aim in data mining, consists of inducing patterns that do not lead to discriminatory decisions even if the original training data sets are biased. Our new data transformation methods (i.e., rule protection and rule generalization (RG)) are based on measures for both direct and indirect discrimination and can deal with several discriminatory items. They provide utility measures. Our approach to discrimination prevention is broader than in previous work. The data transformation methods that can be used for direct and/or indirect discrimination prevention. For each method, its algorithm and its computational cost are specified. Along with privacy, discrimination is a very important issue when considering the legal and ethical aspects of data mining. It is more than obvious that most people do not want to be discriminated because of their gender, religion, nationality, age, and so on, especially when those attributes are used for making decisions about them like giving them a job, loan, insurance. The purpose of this paper was to develop a new preprocessing discrimination prevention methodology including different data transformation methods that can prevent direct discrimination, indirect discrimination or both of them at the same time. To attain this objective, the first step is to measure discrimination and identify categories and groups of individuals that have been directly and/or indirectly discriminated in the decision-making processes; the second step is to transform data in the proper way to remove all those discriminatory biases .