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k-NN as an implementation of situation testing for discrimination discovery and prevention

Online:21 August 2011Publication History

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 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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    • Published in

      ACM Conferences cover image
      KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
      August 2011
      1446 pages
      ISBN:9781450308137
      DOI:10.1145/2020408

      Copyright © 2011 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Online: 21 August 2011
      • Published: 21 August 2011

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