A Methodology For Direct and Indirect Discrimination Prevention In Data Mining

12 citations recorded by the source corpus.

Subfields: Law

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On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products (2016, background): “In certain prediction pro blems, the risk of harm for members of protected groups should not be much worse (up to a multiplicative factor) than the risk of ha rm for others [36]–[38].”
Discrimination- and privacy-aware patterns (2015, background): “, the transformation of data, instead of patterns, aimed to fulfill the privacy-preserving and discrimination-preventing constraints; we have published preliminary exploratory work on this subject in Hajian and Domingo-Ferrer (2012), Hajian et al.”
Using sensitive personal data may be necessary for avoiding discrimination in data-driven decision models (2016, background): “In EU law, this is assessed only marginally: as long as a difference in treatment has some rationality and is not completely arbitrary, the quality of the underlying reasoning is not further assessed (Gellert et al. 2013; McCrudden and Prechal 2009).”