Interpretable and explainable machine learning methods for predictive process monitoring: a systematic literature review

Abstract 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.