Toward Trustworthy Evaluation of Sustainability Rating Methodologies: A Human-AI Collaborative Framework for Benchmark Dataset Construction
arXiv:2602.17106v1 Announce Type: new 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 (LLMs), and SR-Delta, a discrepancy-analysis procedural framework that surfaces insights for potential adjustments. The framework enables scalable and comparab
arXiv:2602.17106v1 Announce Type: new 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 (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.
Executive Summary
The article proposes a human-AI collaborative framework for constructing trustworthy benchmark datasets to evaluate sustainability rating methodologies. The framework consists of two parts: STRIDE, which provides principled criteria and a scoring system, and SR-Delta, a discrepancy-analysis framework. This approach aims to harmonize sustainability ratings across agencies, enhancing their comparability, credibility, and relevance to decision-making. By leveraging large language models, the framework enables scalable and comparable assessment of sustainability rating methodologies, supporting urgent sustainability agendas.
Key Points
- ▸ Human-AI collaborative framework for evaluating sustainability rating methodologies
- ▸ STRIDE provides principled criteria and a scoring system for benchmark dataset construction
- ▸ SR-Delta offers a discrepancy-analysis framework for surfacing insights and potential adjustments
Merits
Scalability and Comparability
The framework enables scalable and comparable assessment of sustainability rating methodologies, which can lead to more accurate and reliable evaluations.
Demerits
Dependence on Data Quality
The effectiveness of the framework relies heavily on the quality of the data used to construct the benchmark datasets, which can be a limitation if the data is incomplete or biased.
Expert Commentary
The proposed framework represents a significant step towards addressing the inconsistencies and limitations of current sustainability rating methodologies. By leveraging the strengths of human and AI collaboration, the framework can facilitate more accurate and reliable evaluations of corporate sustainability performance. However, its effectiveness will depend on the quality of the data used to construct the benchmark datasets and the ability to address potential biases and discrepancies. As the field of sustainability ratings continues to evolve, this framework can serve as a valuable foundation for further research and development.
Recommendations
- ✓ Further research is needed to validate the framework's effectiveness and identify potential areas for improvement
- ✓ Regulatory bodies and industry stakeholders should consider adopting and refining the framework to promote more accurate and comparable sustainability ratings
