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PhD Proposal

dc.contributor.authorAndreswari, Rachmadita
dc.contributor.editorLaue, Ralf
dc.contributor.editorFahrenkrog-Petersen, Stephan
dc.date.accessioned2024-05-08T08:24:45Z
dc.date.available2024-05-08T08:24:45Z
dc.date.issued2024
dc.description.abstractProcess mining techniques provide operational insights into work processes in various types of organizations. These processes handle sensitive data of customers, patients, students, or citizens and their results impact the lives and careers of these affected persons. So far, much of process mining research has focused on classical dimensions of performance such a cycle time or operational cost. What is missing is a primal consideration of ethical concerns such as fairness. Fairness is a recently researched concept in machine learning, which requires a deeper integration into process mining algorithms. This research addresses this requirement. To this end, it aims to analyze fairness concerns in process mining, to develop new process mining algorithms that integrate fairness concerns, and to evaluate them for their effectiveness. Methodologically, our research will build on guidelines for design science and algorithm engineering research. In this way, we will combine engineering research with empirical evaluations.en
dc.identifier.doi10.18420/EMISA2024_02
dc.identifier.isbn978-3-88579-743-2
dc.identifier.issn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/44014
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofEMISA 2024
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-349
dc.subjectprocess mining
dc.subjectfairness
dc.subjectalgorithm
dc.subjectwork process
dc.titlePhD Proposalen
dc.title.subtitleIntegrating Fairness into Process Mining Algorithmsen
mci.conference.date13–14 March 2024
mci.conference.locationPotsdam
mci.conference.sessiontitleEnterprise Modeling and Information Systems Architecture (EMISA 2024)
mci.reference.pages23-28

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