Predictive Process Monitoring
Revolutionizing Trust & Transparency in AI with XAI
This systematic literature review on explainability and interpretability in predictive process monitoring analyzes a decade of literature, using the PRISMA framework. It distinguishes intrinsically interpretable models from black-box explanations, offering a structured overview of methods and real-world applications. Our findings aim to equip researchers and practitioners with deeper understanding for developing trustworthy, transparent, and effective intelligent systems for predictive process analytics.
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Total Studies Reviewed
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BPIC Dataset Adoption
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PPM Focus: Outcome Prediction
Executive Impact
Addressing the core challenges of AI opacity in business processes, our research delivers actionable insights for immediate and long-term strategic advantage.
The Challenge
The rapid advancement and increasing opacity of AI systems make understanding their "black-box" nature critical, particularly for models trained on complex operational and business process data. This leads to a significant lack of trust and hinders widespread adoption in high-stakes domains.
Our AI Solution
Our systematic literature review on explainability and interpretability in Predictive Process Monitoring (PPM) provides a structured analysis of methods, differentiating between intrinsically interpretable models and advanced black-box techniques requiring post-hoc explanations, across diverse applications.
The Benefit
We 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, bridging critical gaps and outlining clear agendas for future research.
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Total Publications
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Years Covered (2009-2025)
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Interpretable Models Identified
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Black-Box Models Identified
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Application Context
Interpretable & Explainable AI
Evaluation Paradigms
Understanding Real-World Impact (RQ1-RQ3)
This section outlines the diverse application domains, benchmark datasets, and predictive tasks observed in explainable PPM research. Key domains include finance, healthcare, customer support, and manufacturing, with a significant portion of studies being domain-agnostic. The BPIC series, particularly BPIC 2012, is the most frequently used dataset, fostering comparability but also highlighting a reliance on specific data types. Prediction tasks predominantly involve process outcome, next event, and time-related PPIs, revealing the versatility of PPM techniques.
Models & Explanation Techniques (RQ4-RQ5)
We differentiate between intrinsically interpretable models like Decision Trees, Bayesian Networks, and Linear/Logistic Regression, favored for their transparency in critical domains, and black-box models such as Deep Learning (DNN, RNN, LSTM), Gradient Boosting Machines, and Random Forests, chosen for their superior predictive performance on complex datasets. The review also surveys post-hoc explanation methods like Counterfactuals, ICE plots, LIME, SHAP, Feature Importance, and Partial Dependence Plots, classifying them by scope (local vs. global) and model relation (agnostic vs. specific).
Assessing Explanation Quality (RQ6-RQ8)
Evaluation practices vary significantly, highlighting a critical gap where predictive performance often overshadows rigorous explanation assessment. The analysis contrasts quantitative metrics (fidelity, stability, sparsity) with qualitative user studies (usefulness, clarity, trust). Evaluation paradigms are categorized into functional, application-grounded, and human-grounded, with the latter being notably scarce. This indicates a need for more holistic, multi-faceted evaluations to ensure XAI systems are meaningful, reliable, and truly useful to human stakeholders.
Systematic Literature Review Process Flow (PRISMA Framework)
Records identified from databases and registers (n=1415)
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Records screened (n=1415)
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Reports sought for retrieval (n=136)
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Reports assessed for eligibility (n=136)
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Studies included in review (n=107)
Calculate Your Potential AI ROI
Estimate the annual savings and efficiency gains your organization could achieve by implementing intelligent process monitoring.
Our Future Research Agenda
Based on identified gaps, our roadmap focuses on advancing XAI in Predictive Process Monitoring, moving from generation to meaningful interaction and practical implementation.
Phase 1: XAI & Trustworthy AI Integration
Develop robust frameworks that combine XAI with uncertainty quantification, privacy-preserving techniques, and fairness-aware process monitoring to enhance trustworthiness and regulatory compliance.
Phase 2: LLM Integration for Natural Language Explanations
Explore the transformative opportunities of Large Language Models (LLMs) to provide natural language interfaces, multi-modal explanation generation, and domain-specific knowledge integration for process models.
Phase 3: Real-time XAI for Stream Event Data
Address the critical need for low-latency, adaptive explanation systems for streaming data, enabling real-time insights into process behaviors and supporting dynamic decision-making in evolving environments.
Phase 4: Object-Centric XAI Frameworks
Develop novel explanation approaches tailored for object-centric process mining, addressing multi-object dependencies, cross-object causal relationships, and behavioral patterns within complex systems.
Phase 5: Holistic Evaluation Methodologies
Establish comprehensive evaluation frameworks integrating quantitative metrics, qualitative user studies, and functional, application-grounded, and human-grounded paradigms to validate XAI effectiveness and utility.
Ready to Transform Your Processes?
Don't let black-box AI hinder your operational efficiency and trust. Partner with us to implement interpretable and explainable AI solutions tailored to your enterprise.
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