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.