用大模型增强标准流程，让AI决策过程透明可审计。
Increasing AI Explainability by LLM Driven Standard Processes
- 将大模型嵌入QOC、敏感性分析等标准决策框架中
- 在去中心化治理等场景复现人类级决策逻辑
- 适合需要可解释性与可验证性的高风险决策应用
本文提出一种通过将大语言模型（LLM）嵌入标准化分析流程来提升人工智能系统可解释性的方法。传统可解释人工智能（XAI）侧重特征归因或事后解释，而该框架将LLM整合到问题-选项-准则（QOC）、敏感性分析、博弈论和风险管理等确定性决策模型中。通过将大模型推理置于这些正式结构内，实现从黑箱推断到可追溯、可审计决策路径的转变。提出分层架构，将大模型的推理空间与上层可解释流程空间分离。实证评估显示，该系统在去中心化治理、系统分析和战略推理场景中能复现人类级别的决策逻辑。结果表明，基于大模型的标准流程为可靠、可解释且可验证的AI辅助决策提供了基础。
原文摘要 · Abstract (English)
This paper introduces an approach to increasing the explainability of artificial intelligence (AI) systems by embedding Large Language Models (LLMs) within standardized analytical processes. While traditional explainable AI (XAI) methods focus on feature attribution or post-hoc interpretation, the proposed framework integrates LLMs into defined decision models such as Question-Option-Criteria (QOC), Sensitivity Analysis, Game Theory, and Risk Management. By situating LLM reasoning within these formal structures, the approach transforms opaque inference into transparent and auditable decision traces. A layered architecture is presented that separates the reasoning space of the LLM from the explainable process space above it. Empirical evaluations show that the system can reproduce human-level decision logic in decentralized governance, systems analysis, and strategic reasoning contexts. The results suggest that LLM-driven standard processes provide a foundation for reliable, interpretable, and verifiable AI-supported decision making.
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