Data mining approaches for discrimination discovery unveil contexts of possible discrimination against protected-by-law groups by extracting classication rules from a dataset of historical decision records. Rules are ranked according to some legally-grounded contrast measure dened over a 4- fold contingency table, including risk dierence, 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

A study of top-k measures for discrimination discovery

Pedreschi D;
2012

Abstract

Data mining approaches for discrimination discovery unveil contexts of possible discrimination against protected-by-law groups by extracting classication rules from a dataset of historical decision records. Rules are ranked according to some legally-grounded contrast measure dened over a 4- fold contingency table, including risk dierence, 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
2012
Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" - ISTI
978-1-4503-0857-1
Discrimination discovery
Classes
Database Applications
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/261771
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