2008 DiscriminationAwareDataMining

• (Pedreshi et al., 2008) ⇒ Dino Pedreshi, Salvatore Ruggieri, and Franco Turini. (2008). “Discrimination-aware Data Mining.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401959

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• http://scholar.google.com/scholar?q=%22Discrimination-aware+data+mining%22+2008
• http://portal.acm.org/citation.cfm?doid=1401890.1401959&preflayout=flat#citedby

Quotes

Abstract

In the context of civil rights law, discrimination refers to unfair or unequal treatment of people based on membership to a category or a minority, without regard to individual merit. Rules extracted from databases by data mining techniques, such as classification or association rules, when used for decision tasks such as benefit or credit approval, can be discriminatory in the above sense. In this paper, the notion of discriminatory classification rules is introduced and studied. Providing a guarantee of non-discrimination is shown to be a non trivial task. A naive approach, like taking away all discriminatory attributes, is shown to be not enough when other background knowledge is available. Our approach leads to a precise formulation of the redlining problem along with a formal result relating discriminatory rules with apparently safe ones by means of background knowledge. An empirical assessment of the results on the German credit dataset is also provided.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 DiscriminationAwareDataMining	Salvatore Ruggieri Dino Pedreshi Franco Turini			Discrimination-aware Data Mining				10.1145/1401890.1401959