Bayesian fairness

dc.contributor.authorBelfrage, Amanda
dc.contributor.authorBerg Marklund, David
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data och informationstekniksv
dc.contributor.examinerHaghir Chehreghani, Morteza
dc.contributor.supervisorDimitrakakis, Christos
dc.date.accessioned2020-07-08T10:57:23Z
dc.date.available2020-07-08T10:57:23Z
dc.date.issued2020sv
dc.date.submitted2020
dc.description.abstractThis thesis aims to extend the Bayesian fairness algorithm created by Dimitrakakis et al. to be able to handle continuous data. Using bagging to approximate the data we aim to reduce the problem to a computable task that still performs well enough to be an improvement over using the true underlying data. Even though promising results where found for using bagging with discrete data, the continuous version of the algorithm did not work.sv
dc.identifier.coursecodeDATX05sv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/301398
dc.language.isoengsv
dc.setspec.uppsokTechnology
dc.subjectFairnesssv
dc.subjectBayesian fairnesssv
dc.subjectalgorithmsv
dc.titleBayesian fairnesssv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH
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