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Interpretable and Explainable Machine Learning Methods for Predictive Process Monitoring: A Systematic Literature Review
This paper presents a systematic literature review (SLR) on the explainability and interpretability of machine learning (ML) models within the context of predictive process mining, using the PRISMA framework.
Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective
This paper introduces a comprehensive, multi-stage machine learning methodology that effectively integrates information systems and artificial intelligence to enhance decision-making processes within the domain of operations research.
Communicating Uncertainty in Machine Learning Explanations: A Visualization Analytics Approach for Predictive Process Monitoring
As data-driven intelligent systems advance, the need for reliable and transparent decision-making mechanisms has become increasingly important.
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