k-NN as an implementation of situation testing for discrimination discovery and prevention (Contributo in atti di convegno)

Type
Label
  • k-NN as an implementation of situation testing for discrimination discovery and prevention (Contributo in atti di convegno) (literal)
Anno
  • 2011-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1145/2020408.2020488 (literal)
Alternative label
  • Luong, Binh Thanh; Ruggieri, Salvatore; Turini, Franco (2011)
    k-NN as an implementation of situation testing for discrimination discovery and prevention
    in 17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '11, San Diego, California, USA, August 21-24 2011
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Luong, Binh Thanh; Ruggieri, Salvatore; Turini, Franco (literal)
Pagina inizio
  • 502 (literal)
Pagina fine
  • 510 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Area di valutazione 01 - Scienze matematiche e informatiche ID_PUMA: /cnr.isti/2011-A2-117 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Institute for Advanced Studies, Lucca, Italy; Computer Science Department, University of Pisa; Department of Computer Science, University of Pisa (literal)
Titolo
  • k-NN as an implementation of situation testing for discrimination discovery and prevention (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-1-4503-0813-7 (literal)
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. (literal)
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