Exploring Interpretable Predictive Models for Business Processes

Abstract

There has been a growing interest in the literature on the application of deep learning models for predicting business process behaviour, such as the next event in a case, the time for completion of an event, and the remaining execution trace of a case. Although these models provide high levels of accuracy, their sophisticated internal representations provide little or no understanding about the reason for a particular prediction, resulting in them being used as black-boxes. Consequently, an interpretable model is necessary to enable transparency and empower users to evaluate when and how much they can rely on the models. This paper explores an interpretable and accurate attention-based Long Short Term Memory (LSTM) model for predicting business process behaviour. The interpretable model provides insights into the model inputs influencing a prediction, thus facilitating transparency. An experimental evaluation shows that the proposed model capable of supporting interpretability also provides accurate predictions when compared to existing LSTM models for predicting process behaviour. The evaluation further shows that attention mechanisms in LSTM provide a sound approach to generate meaningful interpretations across different tasks in predictive process analytics.