Publikation
Interpretable and explainable machine learning methods for predictive process monitoring: A systematic literature review
Nijat Mehdiyev; Maxim Majlatow; Peter Fettke
In: Artificial Intelligence Review, Vol. 58, No. 378, Pages 00-00, Springer, 10/2025.
Zusammenfassung
This study presents a systematic literature review on the explainability and interpretability
of machine learning models within the context of predictive process monitoring. Given the
rapid advancement and increasing opacity of artificial intelligence systems, understanding
the "black-box" nature of these technologies has become critical, particularly for models
trained on complex operational and business process data. Using the PRISMA framework,
this review systematically analyzes and synthesizes the literature of the past decade, including recent and forthcoming works from 2025, to provide a timely and comprehensive overview of the field. We differentiate between intrinsically interpretable models and
more complex systems that require post-hoc explanation techniques, offering a structured
panorama of current methodologies and their real-world applications. Through this rigorous bibliographic analysis, our research provides a detailed synthesis of the state of
explainability in predictive process mining, identifying key trends, persistent challenges
and a clear agenda for future research. Ultimately, 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.