PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Pedreschi D., Ruggieri S., Turini F. Discrimination-aware data mining. In: KDD '08 - 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Las Vegas, 24-27 August 2008). Proceedings, pp. 560 - 568. ACM, 2008.
 
 
Abstract
(English)
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 naĻıve 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.
URL: http://dl.acm.org/citation.cfm?id=1401959&bnc=1
DOI: 10.1145/1401890.1401959
Subject Algorithms
Economics
Legal Aspects
Discrimination
Classification Rules
H.2.8 Database Applications. Data Mining


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