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

TL;DR

This systematic literature review examines the current state of explainability and interpretability in machine learning models used for predictive process monitoring, highlighting methodologies, challenges, and future research directions.

Contribution

It provides a comprehensive overview of interpretability techniques in predictive process mining and differentiates between intrinsically interpretable models and post-hoc explanations.

Findings

Identifies key trends in explainability methods for process mining

Highlights challenges in interpreting complex ML models

Suggests future research directions for trustworthy AI in process monitoring

Abstract

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 rapid advancement of artificial intelligence (AI) and ML systems, understanding the "black-box" nature of these technologies has become increasingly critical. Focusing specifically on the domain of process mining, this paper delves into the challenges of interpreting ML models trained with complex business process data. We differentiate between intrinsically interpretable models and those that require post-hoc explanation techniques, providing a comprehensive overview of the current methodologies and their applications across various application domains. Through a rigorous bibliographic analysis, this research offers a detailed synthesis of the state of explainability and interpretability in predictive process mining, identifying key trends, challenges, and future directions. Our findings aim to equip researchers and practitioners with a deeper understanding of how to develop and implement more trustworthy, transparent, and effective intelligent systems for predictive process analytics.