Explainable Artificial Intelligence for Improved Modeling of Processes
This work addresses the problem of integrating diverse data sources into interpretable process models for business analysts, though it appears incremental by applying existing ML methods to a new domain.
The paper tackled the challenge of modeling business processes by evaluating Transformer architectures and classical ML technologies to predict process regularities, demonstrating their efficacy on five benchmark datasets with improved prediction capabilities and insights from attention mechanisms and XAI components.
In modern business processes, the amount of data collected has increased substantially in recent years. Because this data can potentially yield valuable insights, automated knowledge extraction based on process mining has been proposed, among other techniques, to provide users with intuitive access to the information contained therein. At present, the majority of technologies aim to reconstruct explicit business process models. These are directly interpretable but limited concerning the integration of diverse and real-valued information sources. On the other hand, Machine Learning (ML) benefits from the vast amount of data available and can deal with high-dimensional sources, yet it has rarely been applied to being used in processes. In this contribution, we evaluate the capability of modern Transformer architectures as well as more classical ML technologies of modeling process regularities, as can be quantitatively evaluated by their prediction capability. In addition, we demonstrate the capability of attentional properties and feature relevance determination by highlighting features that are crucial to the processes' predictive abilities. We demonstrate the efficacy of our approach using five benchmark datasets and show that the ML models are capable of predicting critical outcomes and that the attention mechanisms or XAI components offer new insights into the underlying processes.