Sustainability or ESG rating agencies use company disclosures and external data to produce scores or ratings that assess the environmental, social, and governance performance of a company. However, sustainability ratings across agencies for a single company vary widely, limiting their comparability, credibility, and relevance to decision-making. To harmonize the rating results, we propose adopting a universal human-AI collaboration framework to generate trustworthy benchmark datasets for evaluating sustainability rating methodologies. The framework comprises two complementary parts: STRIDE (Sustainability Trust Rating & Integrity Data Equation) provides principled criteria and a scoring system that guide the construction of firm-level benchmark datasets using large language models (LLMs), and SR-Delta, a discrepancy-analysis procedural framework that surfaces insights for potential adjustments. The framework enables scalable and comparable assessment of sustainability rating methodologies. We call on the broader AI community to adopt AI-powered approaches to strengthen and advance sustainability rating methodologies that support and enforce urgent sustainability agendas.
迈向可持续性评级方法的可信评估：一个用于基准数据集构建的人机协作框架 / Toward Trustworthy Evaluation of Sustainability Rating Methodologies: A Human-AI Collaborative Framework for Benchmark Dataset Construction
这篇论文针对当前不同机构给出的企业可持续性（ESG）评分差异巨大、难以比较的问题，提出了一个结合人类专家原则与大型语言模型（LLM）能力的人机协作框架，旨在高效、可扩展地构建可信的基准数据集，从而更可靠地评估和改进各种可持续性评级方法。
源自 arXiv: 2602.17106
