Discrimination-aware data mining

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2008, Pages 560-568

  Pedreshi, Dino ^a   Ruggieri, Salvatore ^a   Turini, Franco ^a  

^a UNIVERSITY OF PISA   (Italy)

Author keywords

Classification rules; Discrimination

Indexed keywords

BACKGROUND KNOWLEDGE; CIVIL RIGHTS; CLASSIFICATION RULES; DATA MINING TECHNIQUES; DATA SETS; DECISION TASKS; DISCRIMINATION; EMPIRICAL ASSESSMENTS; NON-TRIVIAL TASKS;

ASSOCIATIVE PROCESSING; DATA MINING; LAW ENFORCEMENT; MINING;

KNOWLEDGE MANAGEMENT;

EID: 65449163899     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1401890.1401959     Document Type: Conference Paper

References (18)

1. R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In Proc. of VLDB 1994, pages 487-499. Morgan Kaufmann, 1994.
2. R. Agrawal and R. Srikant. Privacy-preserving data mining. In Proc. of SIGMOD 2000, pages 439-450. ACM, 2000.
3. G. S. Becker. The Economics of Discrimination. University of Chicago Press, 1957.
4. C. Clifton. Privacy preserving data mining: How do we mine data when we aren't allowed to see it? Tutorial at KDD 2003. http://www.cs.purdue.edu/ homes/clifton.
5. B. Goethals. Frequent Itemset Mining Implementations Repository, http://fimi.cs.helsinki.fi.
6. H. Holzer, S. Raphael, and M. Stoll. Black job applicants and the hiring officer's race. Industrial and Labor Relations Review, 57(2):267-287, 2004.
7. D.H. Kaye and M. Aickin, editors. Statistical Methods in Discrimination Litigation. Marcel Dekker, Inc., 1992.
8. P. Kuhn. Sex discrimination in labor markets: The role of statistical evidence. The American Economic Review, 77:567-583, 1987.
9. M. LaCour-Little. Discrimination in mortgage lending: A critical review of the literature. J. of Real Estate Literature, 7:15-50, 1999.
10. B. Liu, W. Hsu, and Y. Ma. Integrating classification and association rule mining. In Proc. of KDD 1998, pages 80-86. AAAI Press, 1998.
11. D.J. Newman, S. Hettich, C.L. Blake, and C.J. Merz. UCI repository of machine learning databases, 1998. http ://archive.ics.uci.edu/ml.
12. D. Pedreschi, S. Ruggieri, and F. Turini. Discrimination-aware data mining. Tech. Rep. 07-19, Dip. Inf., Univ. of Pisa, 2007. http://compass2.di. unipi.it/TR.
13. J. Rauch and M. Simunek. Mining for association rules by 4ft-Miner. In Proc. of INAP 2001, pages 285-295. Prolog Association of Japan, 2001. http://lispminer.vse.cz.
14. G. D. Squires. Racial profiling, insurance style: Insurance redlining and the uneven development of metropolitan areas. J. of Urban Affairs, 25(4):391-410, 2003.
15. L. Sweeney. Computational Disclosure Control: A Primer on Data Privacy Protection. PhD thesis, MIT, 2001.
16. P.-N. Tan, V. Kumar, and J. Srivastava. Selecting the right objective measure for association analysis. Inf. Syst., 29(4):293-313, 2004.
17. U.S. Federal Legislation, http://www.usdoj.gov.
18. X. Yin and J. Han. CPAR: Classification based on Predictive Association Rules. In Proc. of SIAM DM 2003, SIAM, 2003.