XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP

TL;DR

XNAP is a novel deep neural network-based method that enhances predictive accuracy in business process monitoring while providing explanations for its predictions using layer-wise relevance propagation, aiding process analysts.

Contribution

This paper introduces XNAP, the first explainable DNN-based technique for next activity prediction in business process monitoring, combining high predictive quality with interpretability.

Findings

XNAP improves prediction accuracy on real-life event logs.

XNAP provides meaningful explanations for its predictions.

The approach supports better decision-making by process analysts.

Abstract

Predictive business process monitoring (PBPM) is a class of techniques designed to predict behaviour, such as next activities, in running traces. PBPM techniques aim to improve process performance by providing predictions to process analysts, supporting them in their decision making. However, the PBPM techniques` limited predictive quality was considered as the essential obstacle for establishing such techniques in practice. With the use of deep neural networks (DNNs), the techniques` predictive quality could be improved for tasks like the next activity prediction. While DNNs achieve a promising predictive quality, they still lack comprehensibility due to their hierarchical approach of learning representations. Nevertheless, process analysts need to comprehend the cause of a prediction to identify intervention mechanisms that might affect the decision making to secure process performance. In this paper, we propose XNAP, the first explainable, DNN-based PBPM technique for the next activity prediction. XNAP integrates a layer-wise relevance propagation method from the field of explainable artificial intelligence to make predictions of a long short-term memory DNN explainable by providing relevance values for activities. We show the benefit of our approach through two real-life event logs.