Abstract
Along with privacy, discrimination is a very important issue when considering the legal and ethical aspects of data mining. It is more than obvious that most people do not want to be discriminated because of their gender, religion, nationality, age and so on, especially when those attributes are used for making decisions about them like giving them a job, loan, insurance, etc. Discovering such potential biases and eliminating them from the training data without harming their decision-making utility is therefore highly desirable. For this reason, anti-discrimination techniques including discrimination discovery and prevention have been introduced in data mining. Discrimination prevention consists of inducing patterns that do not lead to discriminatory decisions even if the original training datasets are inherently biased. In this chapter, by focusing on the discrimination prevention, we present a taxonomy for classifying and examining discrimination prevention methods. Then, we introduce a group of pre-processing discrimination prevention methods and specify the different features of each approach and how these approaches deal with direct or indirect discrimination. A presentation of metrics used to evaluate the performance of those approaches is also given. Finally, we conclude our study by enumerating interesting future directions in this research body.
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Hajian, S., Domingo-Ferrer, J. (2013). Direct and Indirect Discrimination Prevention Methods. In: Custers, B., Calders, T., Schermer, B., Zarsky, T. (eds) Discrimination and Privacy in the Information Society. Studies in Applied Philosophy, Epistemology and Rational Ethics, vol 3. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30487-3_13
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DOI: https://doi.org/10.1007/978-3-642-30487-3_13
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