XABPs: Towards eXplainable Autonomous Business Processes

TL;DR

This paper advocates for eXplainable Autonomous Business Processes (XABPs) to enhance trust, accountability, and compliance in AI-driven workflows by systematically characterizing and structuring explainability in business process management.

Contribution

It introduces a systematic approach to XABPs, defining their forms, structuring explainability, and identifying key research challenges in BPM for autonomous processes.

Findings

Proposes a framework for XABPs in BPM.

Identifies key challenges for implementing explainability.

Highlights benefits of XABPs for trust and compliance.

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.