2022
DOI: 10.1007/978-3-031-08848-3_12
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Responsible Process Mining

Abstract: The prospect of data misuse negatively affecting our life has lead to the concept of responsible data science. It advocates for responsibility to be built, by design, into data management, data analysis, and algorithmic decision making techniques such that it is made difficult or even impossible to intentionally or unintentionally cause harm. Process mining techniques are no exception to this and may be misused and lead to harm. Decisions based on process mining may lead to unfair decisions causing harm to peo… Show more

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Cited by 14 publications
(13 citation statements)
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References 67 publications
(102 reference statements)
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“…Explainability techniques are a way to implement responsible process decision making (see [30]) and can help us to this aim. Different explainability techniques have been proposed in the XAI (Explainable Artificial Intelligence) literature.…”
Section: Explainable Predictionsmentioning
confidence: 99%
“…Explainability techniques are a way to implement responsible process decision making (see [30]) and can help us to this aim. Different explainability techniques have been proposed in the XAI (Explainable Artificial Intelligence) literature.…”
Section: Explainable Predictionsmentioning
confidence: 99%
“…•Control-flow discovery [1,19,37] •Data-aware process discovery [20,23] •Activity mining [10,17,21,28,27,34,36] •Trace clustering [31,6] •Multi-perspective trace clustering [14] •IoT context-aware trace clustering •Multi-perspective process discovery [22] •IoT-enhanced dataaware process discovery [2] Conformance checking…”
Section: Discoverymentioning
confidence: 99%
“…Table A.1: Activity description of the hospital billing event log taken from [35] Activity Description NEW A new billing package is created.…”
Section: Remove Entries Of Oldest Casesmentioning
confidence: 99%