为建筑领域AI决策系统设计可解释性框架，强调证据支撑
Integrating Evidence into the Design of XAI and AI-based Decision Support Systems: A Means-End Framework for End-users in Construction
- 基于证据的因果链框架，提升解释可信度
- 评估证据强度、相关性与实用性，增强推荐可靠性
- 适合建筑从业者、开发者及监管者使用
可解释人工智能旨在使AI模型的推理过程透明可懂，尤其在复杂决策环境中。在日益采用AI决策支持系统的建筑行业，对支撑AI输出可靠性和问责性的证据整合关注不足。缺乏此类证据会削弱解释的有效性与系统建议的可信度。本文通过叙事性综述构建一个理论性、基于证据的手段-目的框架，为设计可解释性增强的决策支持系统提供认识论基础，确保生成的解释契合用户知识需求与决策情境。框架聚焦于评估不同类型证据在支持AI解释时的强度、相关性与实用性。虽以建筑专业人士为主要目标用户，但该框架亦适用于开发者、监管机构及项目管理者等具有不同认识目标的群体。
原文摘要 · Abstract (English)
Explainable Artificial Intelligence seeks to make the reasoning processes of AI models transparent and interpretable, particularly in complex decision making environments. In the construction industry, where AI based decision support systems are increasingly adopted, limited attention has been paid to the integration of supporting evidence that underpins the reliability and accountability of AI generated outputs. The absence of such evidence undermines the validity of explanations and the trustworthiness of system recommendations. This paper addresses this gap by introducing a theoretical, evidence based means end framework developed through a narrative review. The framework offers an epistemic foundation for designing XAI enabled DSS that generate meaningful explanations tailored to users knowledge needs and decision contexts. It focuses on evaluating the strength, relevance, and utility of different types of evidence supporting AI generated explanations. While developed with construction professionals as primary end users, the framework is also applicable to developers, regulators, and project managers with varying epistemic goals.
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