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

• URL: http://arxiv.org/abs/2312.17584v1
• Date: Fri, 29 Dec 2023 12:43:43 GMT
• Title: Interpretable and Explainable Machine Learning Methods for Predictive Process Monitoring: A Systematic Literature Review
• Authors: Nijat Mehdiyev, Maxim Majlatow and Peter Fettke
• Abstract summary: This paper presents a systematic review on the explainability and interpretability of machine learning (ML) models within the context of predictive process mining. We provide a comprehensive overview of the current methodologies and their applications across various application domains. 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 process analytics.
• Score: 1.3812010983144802
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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