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Istituto di Scienza e Tecnologie dell'Informazione     
Pedreschi D., Ruggieri S., Turini F. A study of top-k measures for discrimination discovery. In: SAC '12 - 27th Annual ACM Symposium on Applied Computing (Riva del Garda (Trento), Italy, 26-30 March 2012). Proceedings, pp. 126 - 131. ACM, 2012.
 
 
Abstract
(English)
Data mining approaches for discrimination discovery unveil contexts of possible discrimination against protected-by-law groups by extracting classi cation rules from a dataset of historical decision records. Rules are ranked according to some legally-grounded contrast measure de ned over a 4- fold contingency table, including risk di erence, risk ratio, odds ratio, and a few others. Due to time and cost con- straints, however, only the top-k ranked rules are taken into further consideration by an anti-discrimination analyst. In this paper, we study to what extent the sets of top-k ranked rules with respect to any two pairs of measures agree
URL: http://dl.acm.org/citation.cfm?id=2245303
DOI: 10.1145/2245276.2245303
Subject Discrimination discovery
Classes
Algorithms
Legal Aspects
H.2.8 Database Applications


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