# Explainable Predictive Process Monitoring: A User Evaluation

arXiv: https://arxiv.org/abs/2202.07760 · HTML: https://mlsanity.com/paper/2202.07760/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 80 references that are in the map.
- First posted on arXiv 15 Feb 2022.

## Builds on (most cited first, 17 of 17)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?](https://mlsanity.com/paper/2005.01831/) (2020)
- [Predictive Business Process Monitoring with LSTM Neural Networks](https://mlsanity.com/paper/1612.02130/) (2016)
- [Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study](https://mlsanity.com/paper/2002.00772/) (2020)
- [explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning](https://mlsanity.com/paper/1908.00087/) (2019)
- [Predicting Process Behaviour using Deep Learning](https://mlsanity.com/paper/1612.04600/) (2016)
- [Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring](https://mlsanity.com/paper/1805.02896/) (2018)
- [Predictive Monitoring of Business Processes](https://mlsanity.com/paper/1312.4874/) (2013)
- [Predictive Process Monitoring Methods: Which One Suits Me Best?](https://mlsanity.com/paper/1804.02422/) (2018)
- [Explainable Predictive Process Monitoring](https://mlsanity.com/paper/2008.01807/) (2020)
- [Clustering-Based Predictive Process Monitoring](https://mlsanity.com/paper/1506.01428/) (2015)
- [Time and Activity Sequence Prediction of Business Process Instances](https://mlsanity.com/paper/1602.07566/) (2016)
- [Prescriptive Business Process Monitoring for Recommending Next Best Actions](https://mlsanity.com/paper/2008.08693/) (2020)
- [Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction](https://mlsanity.com/paper/2105.07111/) (2021)
- [XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP](https://mlsanity.com/paper/2008.07993/) (2020)
- [Temporal Stability in Predictive Process Monitoring](https://mlsanity.com/paper/1712.04165/) (2017)

## Built on by (most cited first, 3 of 3)

- [An Explainable Decision Support System for Predictive Process Analytics](https://mlsanity.com/paper/2207.12782/) (2022)
- [Nirdizati: an Advanced Predictive Process Monitoring Toolkit](https://mlsanity.com/paper/2210.09688/) (2022)
- [The Role of Explanation Styles and Perceived Accuracy on Decision Making in Predictive Process Monitoring](https://mlsanity.com/paper/2506.16617/) (2025)

## Abstract (arXiv)

Explainability is motivated by the lack of transparency of black-box Machine Learning approaches, which do not foster trust and acceptance of Machine Learning algorithms. This also happens in the Predictive Process Monitoring field, where predictions, obtained by applying Machine Learning techniques, need to be explained to users, so as to gain their trust and acceptance. In this work, we carry on a user evaluation on explanation approaches for Predictive Process Monitoring aiming at investigating whether and how the explanations provided (i) are understandable; (ii) are useful in decision making tasks;(iii) can be further improved for process analysts, with different Machine Learning expertise levels. The results of the user evaluation show that, although explanation plots are overall understandable and useful for decision making tasks for Business Process Management users -- with and without experience in Machine Learning -- differences exist in the comprehension and usage of different plots, as well as in the way users with different Machine Learning expertise understand and use them.