XABPs: Towards eXplainable Autonomous Business Processes

2025-07-31

AI Summary

Autonomous Business Processes (ABPs) enhance operational efficiency and responsiveness but suffer from critical challenges—including lack of trust, debugging complexity, ambiguous accountability, algorithmic bias, and regulatory noncompliance. To address these, this paper introduces the eXplainable Autonomous Business Processes (XABPs) framework. It systematically defines explanation modalities, structures explainability requirements specific to business process management (BPM), and identifies core explainability challenges in ABP contexts. By integrating AI/ML with BPM technologies, the framework embeds self-explaining decision logic to ensure transparent reasoning and end-to-end traceability. We establish a theoretical foundation and taxonomy for XABPs, delineate key technical pathways for trustworthy deployment, and propose evaluation dimensions for explainability. The work provides both theoretical grounding and methodological support for standardizing explainability and advancing its engineering practice in autonomous process systems.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Machine Learning: Transparent, Interpretable, Explainable ML

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Enhancing trust in autonomous business processes
• Addressing debugging and accountability challenges
• Ensuring regulatory compliance and reducing bias

Innovation

• Leveraging AI/ML for autonomous workflows
• Introducing explainability to address trust issues
• Systematic approach to structuring XABPs