Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring

• URL: http://arxiv.org/abs/2607.17783v1
• Date: Mon, 20 Jul 2026 10:15:47 GMT
• Title: Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring
• Abstract summary: We propose a local post-hoc explainability method for deep neural networks in outcome prediction.<n>The method relies on a control-flow-aware segmentation algorithm that partitions a trace into meaningful segments.<n>We demonstrate its usefulness on real-world event logs from a loan application process and an administrative process of a Dutch municipality.
• Score: 2.9503907416726745
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Predictive process monitoring supports the optimization and control of operational business processes by forecasting the future state or outcome of ongoing cases. While deep neural networks have achieved strong performance for these tasks by modeling sequential dependencies in event logs, their black-box nature limits trust and practical adoption. Feature attribution methods are often used to address this, but applying them directly poses a dilemma: event-level attributions impose high computational complexity for long traces, while explanations based on aggregated trace representations often fail to capture the underlying control-flow dynamics. To address this issue, we propose a local post-hoc explainability method for deep neural networks in outcome prediction. The method relies on a control-flow-aware segmentation algorithm that partitions a trace into meaningful segments and supports the computation of segment-level SHAP explanations. This makes it possible to identify which parts of a trace influence a prediction and which change points steer the case toward the predicted outcome. We assess the proposed segmentation method on a synthetic dataset with known process logic, where meaningful change points can be explicitly verified, and we demonstrate its usefulness on real-world event logs from a loan application process and an administrative process of a Dutch municipality.

Related papers

• Monitoring Data-aware Temporal Properties (Extended Version) [56.386411908764494]
  We consider anticipatory monitoring of linear-time properties enriched with an arbitrary SMT theory over finite traces.<n>Anticipatory monitoring in this setting is highly challenging, as the monitoring state depends on both the trace prefix seen so far and all its possible finite continuations.<n>We present and formally prove the correctness of a novel foundational framework for monitoring properties in an expressive fragment offMT.
  arXiv  Detail & Related papers  (2026-05-14T10:23:11Z)
• Combining Abstract Argumentation and Machine Learning for Efficiently Analyzing Low-Level Process Event Streams [18.821902752237204]
  We propose a data/computation-efficient neuro-symbolic approach to the interpretation problem.<n>Considering the urgent need of developing Green AI solutions, we propose a data/computation-efficient neuro-symbolic approach to the problem.
  arXiv  Detail & Related papers  (2025-05-09T08:45:07Z)
• Generating Feasible and Plausible Counterfactual Explanations for Outcome Prediction of Business Processes [45.502284864662585]
  We introduce a data-driven approach, REVISEDplus, to generate plausible counterfactual explanations. First, we restrict the counterfactual algorithm to generate counterfactuals that lie within a high-density region of the process data. We also ensure plausibility by learning sequential patterns between the activities in the process cases.
  arXiv  Detail & Related papers  (2024-03-14T09:56:35Z)
• Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach [57.788675205519986]
  We learn high-quality traces from POMDP executions generated by any solver. We exploit data- and time-efficient Indu Logic Programming (ILP) to generate interpretable belief-based policy specifications. We show that learneds expressed in Answer Set Programming (ASP) yield performance superior to neural networks and similar to optimal handcrafted task-specifics within lower computational time.
  arXiv  Detail & Related papers  (2024-02-29T15:36:01Z)
• Generalizing Backpropagation for Gradient-Based Interpretability [103.2998254573497]
  We show that the gradient of a model is a special case of a more general formulation using semirings. This observation allows us to generalize the backpropagation algorithm to efficiently compute other interpretable statistics.
  arXiv  Detail & Related papers  (2023-07-06T15:19:53Z)
• FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality Assessment [93.09267863425492]
  We argue that understanding both high-level semantics and internal temporal structures of actions in competitive sports videos is the key to making predictions accurate and interpretable. We construct a new fine-grained dataset, called FineDiving, developed on diverse diving events with detailed annotations on action procedures.
  arXiv  Detail & Related papers  (2022-04-07T17:59:32Z)
• Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models [77.34726150561087]
  We define explainability through the interpretability of the explanations and the faithfulness of the explainability model in the field of process outcome prediction. This paper contributes a set of guidelines named X-MOP which allows selecting the appropriate model based on the event log specifications.
  arXiv  Detail & Related papers  (2022-03-30T05:59:50Z)
• Data Augmentation through Expert-guided Symmetry Detection to Improve Performance in Offline Reinforcement Learning [0.0]
  offline estimation of the dynamical model of a Markov Decision Process (MDP) is a non-trivial task. Recent works showed that an expert-guided pipeline relying on Density Estimation methods effectively detects this structure in deterministic environments. We show that the former results lead to a performance improvement when solving the learned MDP and then applying the optimized policy in the real environment.
  arXiv  Detail & Related papers  (2021-12-18T14:32:32Z)
• CoCoMoT: Conformance Checking of Multi-Perspective Processes via SMT (Extended Version) [62.96267257163426]
  We introduce the CoCoMoT (Computing Conformance Modulo Theories) framework. First, we show how SAT-based encodings studied in the pure control-flow setting can be lifted to our data-aware case. Second, we introduce a novel preprocessing technique based on a notion of property-preserving clustering.
  arXiv  Detail & Related papers  (2021-03-18T20:22:50Z)
• Predictive Process Model Monitoring using Recurrent Neural Networks [2.4029798593292706]
  This paper introduces Processes-As-Movies (PAM), a technique that provides a middle ground between predictive monitoring. It does so by capturing declarative process constraints between activities in various windows of a process execution trace. Various recurrent neural network topologies tailored to high-dimensional input are used to model the process model evolution with windows as time steps.
  arXiv  Detail & Related papers  (2020-11-05T13:57:33Z)
• Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring [0.0]
  This study proposes a conceptual framework sought to establish and promote understanding of decision-making environment. This study defines the local regions from the validation dataset by using the intermediate latent space representations. The adopted deep learning classifier achieves a good performance with the Area Under the ROC Curve of 0.94.
  arXiv  Detail & Related papers  (2020-09-04T10:28:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.