Interpretable and Explainable Machine Learning Methods for Predictive Process Monitoring: A Systematic Literature Review
It addresses the need for more trustworthy and transparent AI systems in predictive process analytics for researchers and practitioners, but is incremental as it reviews existing work rather than proposing new methods.
This systematic literature review tackles the problem of understanding 'black-box' machine learning models in predictive process mining by synthesizing current methodologies, differentiating between interpretable models and post-hoc explanation techniques, and identifying key trends and challenges to guide future research and practice.
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.