Building Interpretable Models for Business Process Prediction using Shared and Specialised Attention Mechanisms

TL;DR

This paper introduces interpretable deep learning models for business process prediction using shared and specialised attention mechanisms, enhancing transparency in predictive process analytics.

Contribution

It proposes novel attention-based models with shared and specialised mechanisms to improve interpretability in process prediction models.

Findings

Models achieve high accuracy on real-life datasets.

Shared and specialised attention mechanisms offer different interpretability insights.

Experimental results demonstrate the effectiveness of the proposed models.

Abstract

In this paper, we address the "black-box" problem in predictive process analytics by building interpretable models that are capable to inform both what and why is a prediction. Predictive process analytics is a newly emerged discipline dedicated to providing business process intelligence in modern organisations. It uses event logs, which capture process execution traces in the form of multi-dimensional sequence data, as the key input to train predictive models. These predictive models, often built upon deep learning techniques, can be used to make predictions about the future states of business process execution. We apply attention mechanism to achieve model interpretability. We propose i) two types of attentions: event attention to capture the impact of specific process events on a prediction, and attribute attention to reveal which attribute(s) of an event influenced the prediction; and ii) two attention mechanisms: shared attention mechanism and specialised attention mechanism to reflect different design decisions in when to construct attribute attention on individual input features (specialised) or using the concatenated feature tensor of all input feature vectors (shared). These lead to two distinct attention-based models, and both are interpretable models that incorporate interpretability directly into the structure of a process predictive model. We conduct experimental evaluation of the proposed models using real-life dataset, and comparative analysis between the models for accuracy and interpretability, and draw insights from the evaluation and analysis results.