2024/08/12 by Martin Käppel, Lars Ackermann, Käppel, Martin +5 · 1 citation
Engineering · #68T01 #68T07 #68U35 #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Fault Detection and Control Systems #H.4.2 #I.2.1 #I.2.6 #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2408.07097
openalex publication_date 2024/08/12 · openalex created_date 2024/09/11 · openalex updated_date 2026/07/28
Predictive process monitoring aims to support the execution of a process during runtime with various predictions about the further evolution of a process instance. In the last years a plethora of deep learning architectures have been established as state-of-the-art for different prediction targets, among others the transformer architecture. The transformer architecture is equipped with a powerful attention mechanism, assigning attention scores to each input part that allows to prioritize most relevant information leading to more accurate and contextual output. However, deep learning models largely represent a black box, i.e., their reasoning or decision-making process cannot be understood in detail. This paper examines whether the attention scores of a transformer based next-activity prediction model can serve as an explanation for its decision-making. We find that attention scores in next-activity prediction models can serve as explainers and exploit this fact in two proposed graph-based explanation approaches. The gained insights could inspire future work on the improvement of predictive business process models as well as enabling a neural network based mining of process models from event logs.