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Titlebook: Discrimination and Privacy in the Information Society; Data Mining and Prof Bart Custers,Toon Calders,Tal Zarsky Book 2013 Springer-Verlag

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楼主: hector
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Techniques for Discrimination-Free Predictive Modelsassification the goal is to learn a predictive model that classifies future data objects as accurately as possible, yet the predicted labels should be uncorrelated to a given sensitive attribute. For example, the task could be to learn a gender-neutral model that predicts whether a potential client
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Direct and Indirect Discrimination Prevention Methodsat 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 traini
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The Discovery of Discriminationextraction and filtering on the basis of legally-grounded interestingness measures. The framework is implemented in the publicly available DCUBE tool. As a running example, we use a public dataset on credit scoring.
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Privacy-Preserving Data Mining Techniques: Survey and Challengesdata (centralized or distributed). We comment on the relationship between PPDM and preventing discriminatory use of data mining techniques. We round up the chapter by discussing some of the new, arising challenges before PPDM as a field.
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2192-6255 politics and public administration, and other people who ma.Vast amounts of data are nowadays collected, stored and processed, in an effort to assist in  making a variety of administrative and governmental decisions. These innovative steps considerably improve the speed, effectiveness and quality o
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Zanyar Abdullah,Tahir Çelik,Tolga Celikdata (centralized or distributed). We comment on the relationship between PPDM and preventing discriminatory use of data mining techniques. We round up the chapter by discussing some of the new, arising challenges before PPDM as a field.
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