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Data preprocessing techniques for classification without discrimination
2011
Knowledge and Information Systems
Recently, the following Discrimination-Aware Classification Problem was introduced: Suppose we are given training data that exhibit unlawful discrimination; e.g., toward sensitive attributes such as gender or ethnicity. The task is to learn a classifier that optimizes accuracy, but does not have this discrimination in its predictions on test data. This problem is relevant in many settings, such as when the data are generated by a biased decision process or when the sensitive attribute serves as
doi:10.1007/s10115-011-0463-8
fatcat:4xytwosddjhnhmri5nlovcmcse