The Role of Artificial Intelligence in Enhancing ESG Disclosure Quality in Accounting
ESG情報開示の品質向上における人工知能の役割：会計学の視点から (AI 翻訳)
Jiacheng Liu, Ye Yuan, Zhelun Zhu
🤖 gxceed AI 要約
日本語
本論文は、会計学、金融学、計算言語学の知見を統合し、AI（特にNLPや機械学習）がESG開示の品質（可読性、比較可能性、情報量、信頼性）向上に果たす役割をレビューする。変圧器モデルによる意味分析など最先端手法を用い、AIが測定の拡大、異質なナラティブの調和、グリーンウォッシング検出のプロトタイプに有効であることを示す。一方、因果関係の証拠不足や多言語アプリケーションのバイアス、解釈可能性の問題も指摘し、今後の研究課題としてクロスリンガルベンチマーク、グリーンウォッシングデータセット、AI保証パイロット、解釈可能性基準を提案する。
English
This paper synthesizes interdisciplinary insights from accounting, finance, and computational linguistics to review how AI (especially NLP and ML) enhances ESG disclosure quality across readability, comparability, informativeness, and credibility. It demonstrates AI's efficacy in scaling measurement, harmonizing heterogeneous narratives, and prototyping greenwashing detection using advanced methods like transformer-based models. However, limited causal evidence, multilingual biases, and interpretability deficits are noted. The paper proposes a forward-looking agenda including cross-lingual benchmarking, curated greenwashing datasets, AI-assurance pilots, and interpretability standards.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本ではSSBJ基準の適用開始や有報でのESG情報開示が進む中、AIを活用した開示品質評価は実務上の関心が高い。本レビューは、日本企業が直面する開示の比較可能性やグリーンウォッシング対策にAIがどう貢献できるかを示唆する。
In the global GX context
As global frameworks like ISSB and CSRD demand higher quality ESG disclosures, AI offers a scalable solution for measuring and verifying disclosure attributes. This review provides a comprehensive foundation for researchers and practitioners exploring AI-driven tools for enhancing transparency and combating greenwashing.
👥 読者別の含意
🔬研究者:Provides a structured taxonomy of ESG disclosure quality dimensions and a research agenda for AI applications in sustainability reporting.
🏢実務担当者:Highlights practical AI tools (e.g., NLP for readability, ML for greenwashing detection) that corporate disclosure teams can pilot.
🏛政策担当者:Offers insights on how AI assurance and interpretability standards could be integrated into regulatory frameworks for ESG disclosure.
📄 Abstract（原文）
As corporate sustainability reporting evolves into a pivotal resource for investors, regulators, and stakeholders, the imperative to evaluate and elevate ESG disclosure quality intensifies amid persistent challenges like opacity, inconsistency, and greenwashing. This review synthesizes interdisciplinary insights from accounting, finance, and computational linguistics on artificial intelligence (AI), particularly natural language processing (NLP) and machine learning (ML), as a transformative force in this domain. We delineate ESG disclosure quality across four operational dimensions: readability, comparability, informativeness, and credibility. By integrating cutting-edge methodological innovations (e.g., transformer-based models for semantic analysis), empirical linkages between AI-extracted signals and market/governance outcomes, and normative discussions on AI’s auditing potential, we demonstrate AI’s efficacy in scaling measurement, harmonizing heterogeneous narratives, and prototyping greenwashing detection. Nonetheless, causal evidence linking managerial AI adoption to stakeholder-perceived enhancements remains limited, compounded by biases in multilingual applications and interpretability deficits. We propose a forward-looking agenda, prioritizing cross-lingual benchmarking, curated greenwashing datasets, AI-assurance pilots, and interpretability standards, to harness AI for substantive, equitable improvements in ESG reporting and accountability.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/jrfm19010058first seen 2026-05-05 23:15:48
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。