Discrimination-aware data mining (Contributo in atti di convegno)

Type

• Prodotto della ricerca (Classe)
• Contributo in atti di convegno (Classe)

Label

• Discrimination-aware data mining (Contributo in atti di convegno) (literal)

Anno

• 2008-01-01T00:00:00+01:00 (literal)

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

• 10.1145/1401890.1401959 (literal)

Alternative label

• SALVATORE RUGGIERI; PEDRESCHI DINO; FRANCO TURINI (2008)
  Discrimination-aware data mining
  in 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, Nevada, USA, August 24-27, 2008
  (literal)

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

• SALVATORE RUGGIERI; PEDRESCHI DINO; FRANCO TURINI (literal)

Pagina inizio

• 560 (literal)

Pagina fine

• 568 (literal)

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

• Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (literal)

Rivista

• KDD (Rivista)

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

• 9 (literal)

Note

• Google Scholar (literal)
• Scopu (literal)

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

• Università di Pisa (literal)

Titolo

• Discrimination-aware data mining (literal)

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

• Ying Li; Bing Liu; Sunita Sarawagi (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. 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 (literal)

Prodotto di

• Knowledge Discovery and Data Mining (ICT.P08.009.003) (Modulo)
• Istituto di scienza e tecnologie dell'informazione \"Alessandro Faedo\" (ISTI) (Istituto)

Autore CNR

• FRANCO TURINI (Persona)
• SALVATORE RUGGIERI (Persona)
• DINO PEDRESCHI (Persona)

Incoming links:

Prodotto

• Istituto di scienza e tecnologie dell'informazione \"Alessandro Faedo\" (ISTI) (Istituto)
• Knowledge Discovery and Data Mining (ICT.P08.009.003) (Modulo)

Autore CNR di

• SALVATORE RUGGIERI (Persona)
• DINO PEDRESCHI (Persona)
• FRANCO TURINI (Persona)

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

• KDD (Rivista)