http://www.cnr.it/ontology/cnr/individuo/prodotto/ID185275

Data mining for discrimination discovery (Articolo in rivista)

Type

Articolo in rivista (Classe)
Prodotto della ricerca (Classe)

Label

Data mining for discrimination discovery (Articolo in rivista) (literal)

Anno

2010-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

10.1145/1754428.1754432 (literal)

Alternative label

PEDRESCHI DINO; SALVATORE RUGGIERI; FRANCO TURINI (2010)
Data mining for discrimination discovery
in ACM transactions on knowledge discovery from data; ACM Press, New York (Stati Uniti d'America)
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

PEDRESCHI DINO; SALVATORE RUGGIERI; FRANCO TURINI (literal)

Pagina inizio

1 (literal)

Pagina fine

40 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni

The paper is referred by the publisher as Article 9 in the Volume 4 Issue 2 of the TKDD journal in 2010. See from matters attached. The paper also has 11 pages of on-line appendix with technical details. (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

4 (literal)

Rivista

ACM transactions on knowledge discovery from data (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali

40 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo

2 (literal)

Note

Google Scholar (literal)
ISI Web of Science (WOS) (literal)
Scopus (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

Università di Pisa (literal)

Titolo

Data mining for discrimination discovery (literal)

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. Discrimination in credit, mortgage, insurance, labor market, and education has been investigated by researchers in economics and human sciences. With the advent of automatic decision support systems, such as credit scoring systems, the ease of data collection opens several challenges to data analysts for the fight against discrimination. In this article, we introduce the problem of discovering discrimination through data mining in a dataset of historical decision records, taken by humans or by automatic systems. We formalize the processes of direct and indirect discrimination discovery by modelling protected-by-law groups and contexts where discrimination occurs in a classification rule based syntax. Basically, classification rules extracted from the dataset allow for unveiling contexts of unlawful discrimination, where the degree of burden over protected-by-law groups is formalized by an extension of the lift measure of a classification rule. In direct discrimination, the extracted rules can be directly mined in search of discriminatory contexts. In indirect discrimination, the mining process needs some background knowledge as a further input, for example, census data, that combined with the extracted rules might allow for unveiling contexts of discriminatory decisions. A strategy adopted for combining extracted classification rules with background knowledge is called an inference model. In this article, we propose two inference models and provide automatic procedures for their implementation. An empirical assessment of our results is provided on the German credit dataset and on the PKDD Discovery Challenge 1999 financial dataset. (literal)

Editore