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Discrimination-aware data mining
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Statistical Model for Rough Set Approach to Multicriteria Classification
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Nearest Neighbour Classification with Monotonicity Constraints
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Building Classifiers with Independency Constraints
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
k-NN as an implementation of situation testing for discrimination discovery and prevention
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Discrimination discovery in scientific project evaluation: A case study
Expert Systems with Applications: An International Journal
Discrimination aware classification for imbalanced datasets
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
What's the deal?: identifying online bargains
AWC '13 Proceedings of the First Australasian Web Conference - Volume 144
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In this paper, we investigate how to modify the naive Bayes classifier in order to perform classification that is restricted to be independent with respect to a given sensitive attribute. Such independency restrictions occur naturally when the decision process leading to the labels in the data-set was biased; e.g., due to gender or racial discrimination. This setting is motivated by many cases in which there exist laws that disallow a decision that is partly based on discrimination. Naive application of machine learning techniques would result in huge fines for companies. We present three approaches for making the naive Bayes classifier discrimination-free: (i) modifying the probability of the decision being positive, (ii) training one model for every sensitive attribute value and balancing them, and (iii) adding a latent variable to the Bayesian model that represents the unbiased label and optimizing the model parameters for likelihood using expectation maximization. We present experiments for the three approaches on both artificial and real-life data.