Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Computational disclosure control: a primer on data privacy protection
Computational disclosure control: a primer on data privacy protection
Selecting the right objective measure for association analysis
Information Systems - Knowledge discovery and data mining (KDD 2002)
Integrating induction and deduction for finding evidence of discrimination
Proceedings of the 12th International Conference on Artificial Intelligence and Law
Data mining for discrimination discovery
ACM Transactions on Knowledge Discovery from Data (TKDD)
DCUBE: discrimination discovery in databases
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Three naive Bayes approaches for discrimination-free classification
Data Mining and Knowledge Discovery
Integrating induction and deduction for finding evidence of discrimination
Artificial Intelligence and Law
Rule protection for indirect discrimination prevention in data mining
MDAI'11 Proceedings of the 8th international conference on Modeling decisions for artificial intelligence
More than modelling and hiding: towards a comprehensive view of Web mining and privacy
Data Mining and Knowledge Discovery
Fairness-Aware classifier with prejudice remover regularizer
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Domain driven data mining in human resource management: A review of current research
Expert Systems with Applications: An International Journal
Discrimination discovery in scientific project evaluation: A case study
Expert Systems with Applications: An International Journal
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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.