Discrimination Aware Decision Tree Learning

Abstract--Recently, the following discrimination aware classification problem was introduced: given a labeled dataset and an attribute , find a classifier with high predictive accuracy that at the same time does not discriminate on the basis of the given attribute . This problem is motivated by the fact that often available historic data is biased due to discrimination, e.g., when denotes ethnicity. Using the standard learners on this data may lead to wrongfully biased classifiers, even if the attribute is removed from training data. Existing solutions for this problem consist in "cleaning away" the discrimination from the dataset before a classifier is learned. In this paper we study an alternative approach in which the non-discrimination constraint is pushed deeply into a decision tree learner by changing its splitting criterion and pruning strategy. Experimental evaluation shows that the proposed approach advances the state-of-the-art in the sense that the learned decisi...

• Type: Journal
• Year: 2010
• Where: ICDM
• Authors: Faisal Kamiran, Toon Calders, Mykola Pechenizkiy
• Keywords: Data Mining, Denotes Ethnicity, Discrimination Aware Classification, ICDM 2010, Lower Discrimination