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

2011

Conference article Open Access

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

Luong Binh Thanh, Ruggieri Salvatore, Turini Franco

k-NN classi 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 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.

Source: 17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '11, pp. 502–510, San Diego, California, USA, August 21-24 2011

Publisher: ACM Press, New York, USA

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@inproceedings{oai:it.cnr:prodotti:206443,
	title = {k-NN as an implementation of situation testing for discrimination discovery and prevention},
	author = {Luong Binh Thanh and Ruggieri Salvatore and Turini Franco},
	publisher = {ACM Press, New York, USA},
	doi = {10.1145/2020408.2020488},
	booktitle = {17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '11, pp. 502–510, San Diego, California, USA, August 21-24 2011},
	year = {2011}
}