Journal article

DAAR: A Discrimination-Aware Association Rule Classifier for Decision Support

Ling Luo, Wei Liu, Irena Koprinska, Fang Chen, A Hameurlain (ed.), J Kung (ed.), R Wagner (ed.), S Madria (ed.), T Hara (ed.)

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | SPRINGER-VERLAG BERLIN | Published : 2017


Undesirable correlations between sensitive attributes (such as race, gender or personal status) and the class label (such as recruitment decision and approval of credit card), may lead to biased decision in data analytics. In this paper, we investigate how to build discrimination-aware models even when the available training set is intrinsically discriminating based on the sensitive attributes. We propose a new classification method called Discrimination-Aware Association Rule classifier (DAAR), which integrates a new discrimination-aware measure and an association rule mining algorithm. We evaluate the performance of DAAR on three real datasets from different domains and compare DAAR with t..

View full abstract

University of Melbourne Researchers