Large Language Models (LLMs) are likely to play a prominent role in future
AI-augmented business process management systems (ABPMSs) catering
functionalities across all system lifecycle stages. One such system's
functionality is Situation-Aware eXplainability (SAX), which relates to
generating causally sound and yet human-interpretable explanations that take
into account the process context in which the explained condition occurred. In
this paper, we present the SAX4BPM framework developed to generate SAX
explanations. The SAX4BPM suite consists of a set of services and a central
knowledge repository. The functionality of these services is to elicit the
various knowledge ingredients that underlie SAX explanations. A key innovative
component among these ingredients is the causal process execution view. In this
work, we integrate the framework with an LLM to leverage its power to
synthesize the various input ingredients for the sake of improved SAX
explanations. Since the use of LLMs for SAX is also accompanied by a certain
degree of doubt related to its capacity to adequately fulfill SAX along with
its tendency for hallucination and lack of inherent capacity to reason, we
pursued a methodological evaluation of the quality of the generated
explanations. To this aim, we developed a designated scale and conducted a
rigorous user study. Our findings show that the input presented to the LLMs
aided with the guard-railing of its performance, yielding SAX explanations
having better-perceived fidelity. This improvement is moderated by the
perception of trust and curiosity. More so, this improvement comes at the cost
of the perceived interpretability of the explanation.

通过 LLMs，结合 SAX4BPM 框架，我们提供了在 AI 增强的业务流程管理系统中生成具有更高可信度的基于过程上下文的人类可解释因果关系解释的方法。