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.

翻译：本文基于PRISMA框架，对预测性流程挖掘情境下机器学习模型的可解释性与可理解性进行了系统性文献综述。随着人工智能与机器学习系统的快速发展，理解这些技术的"黑箱"特性变得日益关键。本文聚焦流程挖掘领域，深入探讨了基于复杂业务流程数据训练的机器学习模型在解释方面的挑战。我们区分了内在可解释模型与需要事后解释技术的模型，全面概述了当前方法及其在各类应用领域中的实践。通过严谨的文献计量分析，本研究综合阐述了预测性流程挖掘中可解释性与可理解性的研究现状，识别了关键趋势、挑战与未来方向。研究结果旨在帮助研究人员与实践者更深入地理解如何开发并部署更可信、更透明、更有效的预测性流程分析智能系统。