SpheriCity让城市可持续报告对话更可信，专家可追踪证据、跨文档对比。
SpheriCity: Designing Trustworthy Conversational AI for Sustainability Decision Support
- 以溯源优先设计对话系统，确保回答可追踪来源。
- 专家评测显示透明溯源与上下文解释显著提升信任度。
- 适合城市可持续政策研究者，支持跨报告探索性查询。
我们提出SpheriCity，一个基于专家经验的对话原型，用于支持从可持续发展报告中可信地进行知识整合。城市层面的循环经济评估报告包含大量关于材料、基础设施和政策干预的信息，但其篇幅长且结构异质，使实践者和研究人员在跨文档合成与比较时面临困难。尽管大语言模型（LLM）能加快知识获取与整合，但其推理不透明、存在幻觉及缺乏来源透明性，在高风险的可持续发展场景中引入信任与可解释性风险，需人工验证。SpheriCity通过以溯源为先的对话代理，突出证据可追溯性、结构化整合与交互引导框架，支持跨报告的探索性查询与合成。我们邀请六位可持续发展专家，使用涵盖跨城市比较、政策总结与建议导向任务的代表性查询进行了形成性评审。专家从多个维度评估响应，并提供对系统在可持续知识工作中实用性的定性反馈。结果表明，透明溯源、上下文解释、可解释性以及与专家工作流程的契合度，强烈影响专家的信任与系统有用性判断。本研究贡献：(1) 一个面向可持续知识整合的对话原型；(2) 一种评估高风险知识领域中AI响应的专家基础评价框架；(3) 关于溯源、不确定性传达与工作流集成如何影响专家对可持续决策支持中AI信任的设计洞见。
原文摘要 · Abstract (English)
We present SpheriCity, an expert-grounded conversational prototype designed to support trustworthy knowledge sensemaking from sustainability reports. City-level circularity assessment reports contain rich information about materials, infrastructure, and policy interventions, yet their length and heterogeneous structure make cross-document synthesis and comparison difficult for practitioners and researchers working on circular economy initiatives. While large language models (LLM) promise faster knowledge access and synthesis, their opaque reasoning, hallucinations, and lack of source transparency introduce risks for trust and interpretability, and require verification in high-stakes sustainability contexts. SpheriCity addresses these challenges through a provenance-first conversational agent that foregrounds evidence traceability, structured synthesis, and interaction scaffolds to support exploratory querying and cross-document synthesis across sustainability reports. We conducted a formative expert review with six sustainability experts using representative queries spanning cross-city comparison, policy summarization, and recommendation-oriented tasks. Experts evaluated responses across dimensions and provided qualitative reflections on the system's usefulness for sustainability knowledge work. Our results reveal that transparent sourcing, contextual explanation, interpretability, and alignment with expert workflow strongly shape expert trust and judgments of system usefulness. This work contributes (1) a conversational prototype for sustainability knowledge sensemaking, (2) an expert-grounded evaluation framework for assessing AI responses in high-stakes knowledge domains, and (3) design insights into how provenance, uncertainty communication, and integration in workflow influence expert users' trust in AI assistance for sustainability decision support.
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