document: k-NN as an implementation of situation testing for discrimination discovery and prevention With the support of the legally-grounded methodology of situation testing , we tackle the problems of discrimination discovery and prevention from a dataset of historical decisions by adopting a variant of k-NN classification . A tuple is labeled as discriminated if we can observe a significant difference of treatment among its neighbors belonging to a protected-by-law group and its neighbors not belonging to it . Discrimination discovery boils down to extracting a classification model from the labeled tuples . Discrimination prevention is tackled by changing the decision value for tuples labeled as discriminated before training a classifier . The approach of this paper overcomes legal weaknesses and technical limitations of existing proposals .
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