Explainable Artificial Intelligence Meets Uncertainty Quantification for Predictive Process Monitoring. - Nijat Mehdiyev; Maxim Majlatow; Peter Fettke (PMAI@IJCAI, 2023)

Interpretable and Explainable Machine Learning Methods for Predictive Process Monitoring: A Systematic Literature Review. - Nijat Mehdiyev; Maxim Majlatow; Peter Fettke (CoRR, 2023)

Communicating Uncertainty in Machine Learning Explanations: A Visualization Analytics Approach for Predictive Process Monitoring. - Nijat Mehdiyev; Maxim Majlatow; Peter Fettke (xAI (3), 2024)

Counterfactual Explanations in the Big Picture: An Approach for Process Prediction-Driven Job-Shop Scheduling Optimization. - Nijat Mehdiyev; Maxim Majlatow; Peter Fettke (Cogn. Comput., 2024)

Augmenting post-hoc explanations for predictive process monitoring with uncertainty quantification via conformalized Monte Carlo dropout. - Nijat Mehdiyev; Maxim Majlatow; Peter Fettke (Data Knowl. Eng., 2025)

Integrating permutation feature importance with conformal prediction for robust Explainable Artificial Intelligence in predictive process monitoring. - Nijat Mehdiyev; Maxim Majlatow; Peter Fettke (Eng. Appl. Artif. Intell., 2025)

Quantifying and explaining machine learning uncertainty in predictive process monitoring: an operations research perspective. - Nijat Mehdiyev; Maxim Majlatow; Peter Fettke (Ann. Oper. Res., 2025)

From Theory to Practice: Real-World Use Cases on Trustworthy LLM-Driven Process Modeling, Prediction and Automation. - Peter Pfeiffer; Alexander Rombach; Maxim Majlatow; Nijat Mehdiyev (CoRR, 2025)