PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Luong B. T., Ruggieri S., Turini F. k-NN as an implementation of situation testing for discrimination discovery and prevention. In: KDD '11 - 17th ACM SIGKDD international conference on Knowledge discovery and data mining (San Diego, California, USA, August 21-24 2011). Proceedings, pp. 502 - 510. ACM, 2011.
 
 
Abstract
(English)
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.
DOI: 10.1145/2020408.2020488
Subject Discrimination discovery and prevention, k-NN classi
H.2.8 [Database Applications]: Data Mining
68T01 General


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