用知识图谱自动识别企业环保谎言，结果透明可验证。
EmeraldMind: A Knowledge Graph-Augmented Framework for Greenwashing Detection
- 构建专用知识图谱整合企业可持续报告数据
- 在新数据集上准确率领先，无需微调
- 适合环保监管、投资评估等需要可信判断的场景
随着AI和网络代理在决策中日益普及，设计既能支持可持续发展又可防范虚假信息的智能系统至关重要。绿色洗白（greenwashing），即误导性企业可持续声明，严重阻碍环境进步。为此，我们提出EmeraldMind，一种以事实为核心的框架，通过融合领域专用知识图谱与检索增强生成技术，实现绿色洗白的自动化检测。EmeraldMind从多样化的公司ESG（环境、社会与治理）报告中构建EmeraldGraph，提取通用知识库中常缺失的可验证证据，支持大语言模型进行声明评估。该框架提供以证据为中心的分类结果，给出透明、有依据的判断，并在无法验证时负责任地保持沉默。在新构建的绿色洗白声明数据集上的实验表明，EmeraldMind在准确率、覆盖范围和解释质量方面均优于通用大模型，且无需微调或重新训练。
原文摘要 · Abstract (English)
As AI and web agents become pervasive in decision-making, it is critical to design intelligent systems that not only support sustainability efforts but also guard against misinformation. Greenwashing, i.e., misleading corporate sustainability claims, poses a major challenge to environmental progress. To address this challenge, we introduce EmeraldMind, a fact-centric framework integrating a domain-specific knowledge graph with retrieval-augmented generation to automate greenwashing detection. EmeraldMind builds the EmeraldGraph from diverse corporate ESG (environmental, social, and governance) reports, surfacing verifiable evidence, often missing in generic knowledge bases, and supporting large language models in claim assessment. The framework delivers justification-centric classifications, presenting transparent, evidence-backed verdicts and abstaining responsibly when claims cannot be verified. Experiments on a new greenwashing claims dataset demonstrate that EmeraldMind achieves competitive accuracy, greater coverage, and superior explanation quality compared to generic LLMs, without the need for fine-tuning or retraining.
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