Toward Trustworthy Evaluation of Sustainability Rating Methodologies: A Human-AI Collaborative Framework for Benchmark Dataset Construction
#Sustainability rating#ESG#AI#Human‑AI collaboration#Benchmark dataset#STRIDE#SR‑Delta#Large language models#Rating methodology#Comparability#Transparency
📌 Key Takeaways
Sustainability ratings from different agencies for the same company often diverge, undermining comparability and credibility.
The authors propose a human‑AI collaboration framework to build benchmark datasets for rating methodology evaluation.
STRIDE establishes criteria and a scoring system to guide the creation of firm‑level benchmark datasets using large language models.
SR‑Delta provides a procedural approach to surface discrepancies and inform potential adjustments to rating methods.
The framework enables scalable and comparable assessment of ESG rating methodologies.
The authors call on the AI community to adopt AI‑powered approaches for strengthening sustainability rating practices.
📖 Full Retelling
The study, authored by Xiaoran Cai, Wang Yang, Xiyu Ren, Chekun Law, Rohit Sharma and Peng Qi, introduces a human‑AI collaborative framework aimed at producing trustworthy benchmark datasets for evaluating sustainability rating (ESG) methodologies. It was submitted to the Artificial Intelligence category of arXiv on 19 February 2026 with the intention of addressing the wide variability and limited comparability of sustainability ratings across agencies, thereby enhancing their credibility and relevance for decision‑making. The framework, comprising STRIDE – a principled scoring system for dataset construction powered by large language models – and SR‑Delta – a discrepancy‑analysis procedure for identifying rating gaps – offers a scalable, AI‑enabled approach that the authors advocate the broader AI community to adopt in support of urgent sustainability agendas.
🏷️ Themes
Sustainability & ESG ratings, Artificial intelligence & large language models, Human‑AI collaboration, Benchmark dataset construction, Transparency & comparability in sustainability metrics, Standardization of rating methodologies
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Deep Analysis
Why It Matters
The paper introduces a human-AI framework to create benchmark datasets for sustainability ratings, addressing inconsistencies across ESG agencies. By standardizing evaluation, it enhances credibility and usefulness of ESG scores for investors and regulators.
Context & Background
ESG ratings vary widely across agencies, limiting comparability
Current methods lack a unified benchmark for assessing rating quality
The authors propose STRIDE and SR-Delta to guide dataset construction using large language models
What Happens Next
The AI community is invited to adopt the framework, potentially leading to more reliable ESG ratings. Future work may involve publishing benchmark datasets and integrating them into rating platforms.
Frequently Asked Questions
What is STRIDE?
STRIDE is a scoring system that provides criteria for building firm-level benchmark datasets using large language models.
How does SR-Delta work?
SR-Delta analyzes discrepancies between ratings to surface insights for adjusting methodologies.
Will the benchmark datasets be publicly available?
The authors plan to release the datasets, encouraging open evaluation of ESG rating methods.
Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.17106 [Submitted on 19 Feb 2026] Title: Toward Trustworthy Evaluation of Sustainability Rating Methodologies: A Human-AI Collaborative Framework for Benchmark Dataset Construction Authors: Xiaoran Cai , Wang Yang , Xiyu Ren , Chekun Law , Rohit Sharma , Peng Qi View a PDF of the paper titled Toward Trustworthy Evaluation of Sustainability Rating Methodologies: A Human-AI Collaborative Framework for Benchmark Dataset Construction, by Xiaoran Cai and 5 other authors View PDF HTML Abstract: 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 , 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. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.17106 [cs.AI] (or arXiv:2602.17106v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.17106 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: ...
