让自动业务流程能解释决策理由，提升可信度与合规性。
XABPs: Towards eXplainable Autonomous Business Processes
- 设计可解释的自动业务流程框架，支持决策溯源
- 提出三类可解释形式：逻辑、因果、证据驱动
- 适合需要透明审计的金融、医疗等高监管领域
自主业务流程（ABPs）利用AI/ML实现自执行工作流，有望提升效率、降低错误率与成本，并加速响应。然而，其缺乏透明性可能引发利益相关方信任危机、调试困难、责任不清、偏见风险及合规隐患。本文主张构建可解释的自主业务流程（XABPs），使系统能够阐明决策依据。论文提出系统化方法，界定XABPs的表现形式，建立可解释性结构，并识别关键的业务流程管理（BPM）研究挑战，推动可信自动化发展。
原文摘要 · Abstract (English)
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.
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