2023/04/12 by Nijat Mehdiyev, Mehdiyev, Nijat, Maxim Majlatow +3 · 1 citation
Business, Management and Accounting · Computer Science · #Applications (stat.AP) #Artificial Intelligence (cs.AI) #Big Data and Business Intelligence #Business Process Modeling and Analysis #Data Visualization and Analytics #FOS: Computer and information sciences #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2304.05736
openalex publication_date 2023/04/12 · openalex created_date 2023/04/15 · openalex updated_date 2026/07/28
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.