XABPs: Towards eXplainable Autonomous Business Processes
This addresses trust and compliance issues for stakeholders in business process management, but it is incremental as it builds on existing explainable AI concepts.
The paper tackles the problem of decreased trust and accountability in autonomous business processes (ABPs) by proposing eXplainable ABPs (XABPs) to enable systems to articulate their rationale, outlining a systematic approach to characterize forms, structure explainability, and identify key research challenges.
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.