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 classifi cation. A tuple is labeled as discriminated if we can observe a signi ficant di erence 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 classi fication model from the labeled tuples. Discrimination prevention is tackled by changing the decision value for tuples labeled as discriminated before training a classi fier. The approach of this paper overcomes legal weaknesses and technical limitations of existing proposals.
k-NN as an implementation of situation testing for discrimination discovery and prevention
Ruggieri Salvatore;Turini Franco
2011
Abstract
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 classifi cation. A tuple is labeled as discriminated if we can observe a signi ficant di erence 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 classi fication model from the labeled tuples. Discrimination prevention is tackled by changing the decision value for tuples labeled as discriminated before training a classi fier. The approach of this paper overcomes legal weaknesses and technical limitations of existing proposals.File | Dimensione | Formato | |
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