Fair machine learning: Influence of demographic parity fairness on other fairness measures

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dc.contributor.advisorHeskes, T.M.
dc.contributor.authorJacobs, M.J.F.
dc.date.issued2019-06-28
dc.description.abstractMachine learning is used more and more every day. In machine learning, fairness is a continuously growing field of interest with an increasing number of projects every year. Fairness is involved in machine learning to remove biased outcomes, or at least make it less biased. When making use of fairness, there is often a focus on one fairness definition. In this paper, the influence of Zafar et al.’s implementation (Zafar, 2018) of demographic parity on other fairness measures will be discussed.en_US
dc.embargo.lift10000-01-01
dc.embargo.typePermanent embargoen_US
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/12554
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationBachelor Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeBacheloren_US
dc.titleFair machine learning: Influence of demographic parity fairness on other fairness measuresen_US
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