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

2022-02-16 · Ghada ElKhawaga, Mervat Abuelkheir, Manfred Reichert

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.

📄 PDF Abstract BibTeX arXiv:2202.08041

Code (1)

ghadaelkhawaga/ppm_xai_comparison 공식 구현

Tasks

Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Predictive Process Monitoring

Explainable Predictive Process Monitoring: A User Evaluation

2022-02-15 · Williams Rizzi, Marco Comuzzi, Chiara Di Francescomarino, Chiara Ghidini 외

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 Monit…

BIG-bench Machine Learning Decision Making Management Predictive Process Monitoring

Self-Explaining Neural Networks for Business Process Monitoring

2025-03-23 · Shahaf Bassan, Shlomit Gur, Sergey Zeltyn, Konstantinos Mavrogiorgos 외

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-…

Activity Prediction Predictive Process Monitoring

Interpretable and Explainable Machine Learning Methods for Predictive Process Monitoring: A Systematic Literature Review

2023-12-29 · Nijat Mehdiyev, Maxim Majlatow, Peter Fettke

This paper presents a systematic literature review (SLR) on the explainability and interpretability of machine learning (ML) models within the context of predictive process mining, using the PRISMA framework. Given the r…

Predictive Process Monitoring Systematic Literature Review

Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models

2022-03-30 · Alexander Stevens, Johannes De Smedt

Although a recent shift has been made in the field of predictive process monitoring to use models from the explainable artificial intelligence field, the evaluation still occurs mainly through performance-based metrics, …

Explainable artificial intelligence Predictive Process Monitoring

XAI in the context of Predictive Process Monitoring: Too much to Reveal