mlsanityData to 14 Oct 2026

XABPs: Towards eXplainable Autonomous Business Processes

arXiv 2507.23269 ↗First posted 31 Jul 2025cs.SE cs.AI cs.MAcited by 3

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Abstract (arXiv)

Autonomous business processes (ABPs), i.e., self-executing workflows leveraging AI/ML, have the potential to improve operational efficiency, reduce errors, lower costs, improve response times, and free human workers for more strategic and creative work. However, ABPs may raise specific concerns including decreased stakeholder trust, difficulties in debugging, hindered accountability, risk of bias, and issues with regulatory compliance. We argue for eXplainable ABPs (XABPs) to address these concerns by enabling systems to articulate their rationale. The paper outlines a systematic approach to XABPs, characterizing their forms, structuring explainability, and identifying key BPM research challenges towards XABPs.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 16 of its 34 references that are in the map. First posted on arXiv 31 Jul 2025.

Builds on

16 of 34 references.

References · 16

  1. 2021A systematic literature review on state-of-the-art deep learning methods for process predictioncited by 32
  2. 2022AI-Augmented Business Process Management Systems: A Research Manifestocited by 28
  3. 2020Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoringcited by 20
  4. 2022Counterfactual Explanations for Predictive Business Process Monitoringcited by 12
  5. 2024How well can a large language model explain business processes as perceived by users?cited by 8
  6. 2023The WHY in Business Processes: Discovery of Causal Execution Dependenciescited by 7
  7. 2024Towards a Benchmark for Causal Business Process Reasoning with LLMscited by 7
  8. 2025The "Who", "What", and "How" of Responsible AI Governance: A Systematic Review and Meta-Analysis of (Actor, Stage)-Specific Toolscited by 6
  9. 2022Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Modelscited by 5
  10. 2024Machine learning in business process management: A systematic literature reviewcited by 5
  11. 2023Automatically Reconciling the Trade-off between Prediction Accuracy and Earliness in Prescriptive Business Process Monitoringcited by 4
  12. 2023Interpretable and Explainable Machine Learning Methods for Predictive Process Monitoring: A Systematic Literature Reviewcited by 4
4 more
  1. 2023A User Study on Explainable Online Reinforcement Learning for Adaptive Systemscited by 3
  2. 2025Selecting the Right LLM for eGov Explanationscited by 3
  3. 2022Discrete models of continuous behavior of collective adaptive systemscited by 1
  4. 2023An AI Chatbot for Explaining Deep Reinforcement Learning Decisions of Service-oriented Systemscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2026Agentic Business Process Management: A Research Manifestocited by 8
  2. 2025A Research Roadmap for Augmenting Software Engineering Processes and Software Products with Generative AIcited by 1
  3. 2026On the Hybrid Nature of ABPMS Process Frames and its Implications on Automated Process Discoverycited by 0