# XABPs: Towards eXplainable Autonomous Business Processes

arXiv: https://arxiv.org/abs/2507.23269 · HTML: https://mlsanity.com/paper/2507.23269/

## 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 (most cited first, 16 of 16)

- [A systematic literature review on state-of-the-art deep learning methods for process prediction](https://mlsanity.com/paper/2101.09320/) (2021)
- [AI-Augmented Business Process Management Systems: A Research Manifesto](https://mlsanity.com/paper/2201.12855/) (2022)
- [Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring](https://mlsanity.com/paper/2009.02098/) (2020)
- [Counterfactual Explanations for Predictive Business Process Monitoring](https://mlsanity.com/paper/2202.12018/) (2022)
- [How well can a large language model explain business processes as perceived by users?](https://mlsanity.com/paper/2401.12846/) (2024)
- [The WHY in Business Processes: Discovery of Causal Execution Dependencies](https://mlsanity.com/paper/2310.14975/) (2023)
- [Towards a Benchmark for Causal Business Process Reasoning with LLMs](https://mlsanity.com/paper/2406.05506/) (2024)
- [The "Who", "What", and "How" of Responsible AI Governance: A Systematic Review and Meta-Analysis of (Actor, Stage)-Specific Tools](https://mlsanity.com/paper/2502.13294/) (2025)
- [Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models](https://mlsanity.com/paper/2203.16073/) (2022)
- [Machine learning in business process management: A systematic literature review](https://mlsanity.com/paper/2405.16396/) (2024)
- [Automatically Reconciling the Trade-off between Prediction Accuracy and Earliness in Prescriptive Business Process Monitoring](https://mlsanity.com/paper/2307.05939/) (2023)
- [Interpretable and Explainable Machine Learning Methods for Predictive Process Monitoring: A Systematic Literature Review](https://mlsanity.com/paper/2312.17584/) (2023)
- [A User Study on Explainable Online Reinforcement Learning for Adaptive Systems](https://mlsanity.com/paper/2307.04098/) (2023)
- [Selecting the Right LLM for eGov Explanations](https://mlsanity.com/paper/2504.21032/) (2025)
- [Discrete models of continuous behavior of collective adaptive systems](https://mlsanity.com/paper/2205.00828/) (2022)
- [An AI Chatbot for Explaining Deep Reinforcement Learning Decisions of Service-oriented Systems](https://mlsanity.com/paper/2309.14391/) (2023)

## Built on by (most cited first, 3 of 3)

- [Agentic Business Process Management: A Research Manifesto](https://mlsanity.com/paper/2603.18916/) (2026)
- [A Research Roadmap for Augmenting Software Engineering Processes and Software Products with Generative AI](https://mlsanity.com/paper/2510.26275/) (2025)
- [On the Hybrid Nature of ABPMS Process Frames and its Implications on Automated Process Discovery](https://mlsanity.com/paper/2604.22455/) (2026)

## 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.