# An Explainable Decision Support System for Predictive Process Analytics

arXiv: https://arxiv.org/abs/2207.12782 · HTML: https://mlsanity.com/paper/2207.12782/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 57 references that are in the map.
- First posted on arXiv 26 Jul 2022.

## Builds on (most cited first, 24 of 24)

- [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)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Attention is not Explanation](https://mlsanity.com/paper/1902.10186/) (2019)
- [Is Attention Interpretable?](https://mlsanity.com/paper/1906.03731/) (2019)
- [On the Robustness of Interpretability Methods](https://mlsanity.com/paper/1806.08049/) (2018)
- [CatBoost: gradient boosting with categorical features support](https://mlsanity.com/paper/1810.11363/) (2018)
- [Predictive Business Process Monitoring with LSTM Neural Networks](https://mlsanity.com/paper/1612.02130/) (2016)
- [A systematic review and taxonomy of explanations in decision support and recommender systems](https://mlsanity.com/paper/2006.08672/) (2020)
- [Explainable Predictive Process Monitoring](https://mlsanity.com/paper/2008.01807/) (2020)
- [LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances](https://mlsanity.com/paper/1711.03822/) (2017)
- [DiCE4EL: Interpreting Process Predictions using a Milestone-Aware Counterfactual Approach](https://mlsanity.com/paper/2107.08697/) (2021)
- [XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP](https://mlsanity.com/paper/2008.07993/) (2020)
- [Counterfactual Explanations for Predictive Business Process Monitoring](https://mlsanity.com/paper/2202.12018/) (2022)
- [A Meta Survey of Quality Evaluation Criteria in Explanation Methods](https://mlsanity.com/paper/2203.13929/) (2022)
- [Building Interpretable Models for Business Process Prediction using Shared and Specialised Attention Mechanisms](https://mlsanity.com/paper/2109.01419/) (2021)
- [Cause vs. Effect in Context-Sensitive Prediction of Business Process Instances](https://mlsanity.com/paper/2007.07549/) (2020)
- [The Duo of Artificial Intelligence and Big Data for Industry 4.0: Review of Applications, Techniques, Challenges, and Future Research Directions](https://mlsanity.com/paper/2104.02425/) (2021)
- [Explainable Predictive Process Monitoring: A User Evaluation](https://mlsanity.com/paper/2202.07760/) (2022)
- [Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?](https://mlsanity.com/paper/2202.08041/) (2022)
- [XAI in the context of Predictive Process Monitoring: Too much to Reveal](https://mlsanity.com/paper/2202.08265/) (2022)

## Built on by (most cited first, 8 of 8)

- [How well can a large language model explain business processes as perceived by users?](https://mlsanity.com/paper/2401.12846/) (2024)
- [SNAP: Semantic Stories for Next Activity Prediction](https://mlsanity.com/paper/2401.15621/) (2024)
- [Outcome-Oriented Prescriptive Process Monitoring Based on Temporal Logic Patterns](https://mlsanity.com/paper/2211.04880/) (2022)
- [Interpretable and Explainable Machine Learning Methods for Predictive Process Monitoring: A Systematic Literature Review](https://mlsanity.com/paper/2312.17584/) (2023)
- [Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring](https://mlsanity.com/paper/2403.11642/) (2024)
- [The WHY in Business Processes: Unification of Causal Process Models](https://mlsanity.com/paper/2505.22871/) (2025)
- [The Role of Explanation Styles and Perceived Accuracy on Decision Making in Predictive Process Monitoring](https://mlsanity.com/paper/2506.16617/) (2025)
- [Exploring LLM Features in Predictive Process Monitoring for Small-Scale Event-Logs](https://mlsanity.com/paper/2601.11468/) (2026)

## Abstract (arXiv)

Predictive Process Analytics is becoming an essential aid for organizations, providing online operational support of their processes. However, process stakeholders need to be provided with an explanation of the reasons why a given process execution is predicted to behave in a certain way. Otherwise, they will be unlikely to trust the predictive monitoring technology and, hence, adopt it. This paper proposes a predictive analytics framework that is also equipped with explanation capabilities based on the game theory of Shapley Values. The framework has been implemented in the IBM Process Mining suite and commercialized for business users. The framework has been tested on real-life event data to assess the quality of the predictions and the corresponding evaluations. In particular, a user evaluation has been performed in order to understand if the explanations provided by the system were intelligible to process stakeholders.