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

翻译：随着数据驱动智能系统的发展，对可靠且透明的决策机制的需求日益重要。因此，有必要整合不确定性量化与模型可解释性方法，以构建可信赖的业务与运营过程分析。本研究探讨了如何在全局和局部的后验解释方法（如部分依赖图（PDP）和个体条件期望（ICE）图）中有效传达模型不确定性。同时，本研究考察了合适的可视化分析方法以促进此类方法论的整合。通过结合这两个研究方向，决策者不仅能论证解释驱动型可行洞察的合理性，还能验证其可靠性。最后，本研究通过专家访谈评估了所提方法及设计的界面在制造业领域真实预测过程监控问题中的适用性。