Self-Explaining Neural Networks for Business Process Monitoring

• URL: http://arxiv.org/abs/2503.18067v1
• Date: Sun, 23 Mar 2025 13:28:34 GMT
• Title: Self-Explaining Neural Networks for Business Process Monitoring
• Authors: Shahaf Bassan, Shlomit Gur, Sergey Zeltyn, Konstantinos Mavrogiorgos, Ron Eliav, Dimosthenis Kyriazis,
• Abstract summary: We introduce, to the best of our knowledge, the first *self-explaining neural network* architecture for predictive process monitoring.<n>Our framework trains an LSTM model that not only provides predictions but also outputs a concise explanation for each prediction.<n>We show that our method outperforms post-hoc approaches in terms of both the faithfulness of the generated explanations and substantial improvements in efficiency.
• Score: 2.8499886197917443
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tasks in Predictive Business Process Monitoring (PBPM), such as Next Activity Prediction, focus on generating useful business predictions from historical case logs. Recently, Deep Learning methods, particularly sequence-to-sequence models like Long Short-Term Memory (LSTM), have become a dominant approach for tackling these tasks. However, to enhance model transparency, build trust in the predictions, and gain a deeper understanding of business processes, it is crucial to explain the decisions made by these models. Existing explainability methods for PBPM decisions are typically *post-hoc*, meaning they provide explanations only after the model has been trained. Unfortunately, these post-hoc approaches have shown to face various challenges, including lack of faithfulness, high computational costs and a significant sensitivity to out-of-distribution samples. In this work, we introduce, to the best of our knowledge, the first *self-explaining neural network* architecture for predictive process monitoring. Our framework trains an LSTM model that not only provides predictions but also outputs a concise explanation for each prediction, while adapting the optimization objective to improve the reliability of the explanation. We first demonstrate that incorporating explainability into the training process does not hurt model performance, and in some cases, actually improves it. Additionally, we show that our method outperforms post-hoc approaches in terms of both the faithfulness of the generated explanations and substantial improvements in efficiency.

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