# Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?

arXiv: https://arxiv.org/abs/2202.08041 · HTML: https://mlsanity.com/paper/2202.08041/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 16 Feb 2022.

## Builds on (most cited first, 11 of 11)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Model-Agnostic Interpretability of Machine Learning](https://mlsanity.com/paper/1606.05386/) (2016)
- [Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models](https://mlsanity.com/paper/1612.08468/) (2016)
- [Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers](https://mlsanity.com/paper/1604.00825/) (2016)
- [Principles and Practice of Explainable Machine Learning](https://mlsanity.com/paper/2009.11698/) (2020)
- [Outcome-Oriented Predictive Process Monitoring: Review and Benchmark](https://mlsanity.com/paper/1707.06766/) (2017)
- [Explainable Predictive Process Monitoring](https://mlsanity.com/paper/2008.01807/) (2020)
- [Clustering-Based Predictive Process Monitoring](https://mlsanity.com/paper/1506.01428/) (2015)
- [XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP](https://mlsanity.com/paper/2008.07993/) (2020)

## 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)
- [Trace Encoding in Process Mining: a survey and benchmarking](https://mlsanity.com/paper/2301.02167/) (2023)
- [Predictive Compliance Monitoring in Process-Aware Information Systems: State of the Art, Functionalities, Research Directions](https://mlsanity.com/paper/2205.05446/) (2022)

## Abstract (arXiv)

Predictive business process monitoring (PPM) has been around for several years as a use case of process mining. PPM enables foreseeing the future of a business process through predicting relevant information about how a running process instance might end, related performance indicators, and other predictable aspects. A big share of PPM approaches adopts a Machine Learning (ML) technique to address a prediction task, especially non-process-aware PPM approaches. Consequently, PPM inherits the challenges faced by ML approaches. One of these challenges concerns the need to gain user trust in the predictions generated. The field of explainable artificial intelligence (XAI) addresses this issue. However, the choices made, and the techniques employed in a PPM task, in addition to ML model characteristics, influence resulting explanations. A comparison of the influence of different settings on the generated explanations is missing. To address this gap, we investigate the effect of different PPM settings on resulting data fed into an ML model and consequently to a XAI method. We study how differences in resulting explanations may indicate several issues in underlying data. We construct a framework for our experiments including different settings at each stage of PPM with XAI integrated as a fundamental part. Our experiments reveal several inconsistencies, as well as agreements, between data characteristics (and hence expectations about these data), important data used by the ML model as a result of querying it, and explanations of predictions of the investigated ML model.