Communicating Uncertainty in Machine Learning Explanations: A Visualization Analytics Approach for Predictive Process Monitoring
This work addresses the need for trustworthy decision-making in business and operational processes by enhancing transparency in predictive analytics, though it appears incremental as it combines existing uncertainty and explainability methods.
The study tackled the problem of communicating model uncertainty in machine learning explanations for predictive process monitoring, by integrating uncertainty quantification with visualization analytics in global and local post-hoc methods like PDP and ICE plots, and validated the approach through expert interviews in a manufacturing domain.
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.