Explainable Artificial Intelligence for Improved Modeling of Processes
- URL: http://arxiv.org/abs/2212.00695v1
- Date: Thu, 1 Dec 2022 17:56:24 GMT
- Title: Explainable Artificial Intelligence for Improved Modeling of Processes
- Authors: Riza Velioglu, Jan Philip G\"opfert, Andr\'e Artelt, Barbara Hammer
- Abstract summary: We evaluate the capability of modern Transformer architectures and more classical Machine Learning technologies of modeling process regularities.
We 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.
- Score: 6.29494485203591
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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