Calibrating Credibility and Auditability of Generative Disclosures in Sustainability Reporting

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Researchers in AI for ESG reporting can leverage the integrated model (ERNIE, DeBERTa, SLCI) and the GAI-CN index as a baseline for cross-country comparisons.

🏢実務担当者:Corporate sustainability teams can use the credibility calibration approach to enhance auditability of AI-assisted disclosures.

🏛政策担当者:Regulators exploring AI disclosure frameworks should note the positive link between auditability and governance quality, supporting the need for robust AI governance.

In face of the increasing trend of integrating generative artificial intelligence in sustainability reporting, it can be seen that the primary concern with respect to disclosure efficiency and credibility has increasingly been addressed by regulators and academia alike. The purpose of the current study is to resolve the issues of credibility and auditability with respect to corporate disclosures made via AI in China with the help of a credibility calibration and audit quantification model with a two-tiered structure. Based on 71 sets of ESG disclosure data from Chinas A-share listed companies in 20202024, the study integrated ERNIE, DeBERTa, and SLCI models for semantic alignment and credibility evaluation in corporate disclosures, and further proposed the application of the Generative Auditability IndexChina (GAI-CN) to quantify traceability, transparency, and regulatory compliance in corporate disclosures. The empirical result shows that there was a 21.0% improvement in credibility calibration with significant positive correlations between auditability and governance quality with an R=0.63, p < 0.01 significance level.

• openaire https://doi.org/10.54254/2755-2721/2025.30135first seen 2026-05-14 22:27:09