document: Discrimination-aware data mining In the context of civil rights law , discrimination refers to unfair or unequal treatment of people based on membership to a category or a minority , without regard to individual merit . Rules extracted from databases by data mining techniques , such as classification or association rules , when used for decision tasks such as benefit or credit approval , can be discriminatory in the above sense . In this paper , the notion of discriminatory classification rules is introduced and studied . Providing a guarantee of non-discrimination is shown to be a non trivial task . A naive approach , like taking away all discriminatory attributes , is shown to be not enough when other background knowledge is available . Our approach leads to a precise formulation of the redlining problem along with a formal result relating discriminatory rules with apparently safe ones by means of background knowledge . An empirical assessment of the results on the German credit dataset is also provided .
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