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As data-driven intelligent systems advance, the need for reliable andtransparent decision-making mechanisms has become increasingly important.Therefore, it is essential to integrate uncertainty quantification and modelexplainability approaches to foster trustworthy business and operationalprocess analytics. This study explores how model uncertainty can be effectivelycommunicated in global and local post-hoc explanation approaches, such asPartial Dependence Plots (PDP) and Individual Conditional Expectation (ICE)plots. In addition, this study examines appropriate visualization analyticsapproaches to facilitate such methodological integration. By combining thesetwo research directions, decision-makers can not only justify the plausibilityof explanation-driven actionable insights but also validate their reliability.Finally, the study includes expert interviews to assess the suitability of theproposed approach and designed interface for a real-world predictive processmonitoring problem in the manufacturing domain.

Communicating Uncertainty in Machine Learning Explanations: A Visualization Analytics Approach for Predictive Process Monitoring