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AI活用ESG報告システム：グリーンウォッシュ2.0の検出と統治のためのフレームワーク

AI-Enabled ESG Reporting Systems: A Framework for Detecting and Governing Greenwashing 2.0 (原題)

Gurazada Himaja Veda Sri, Hamsika Madanu, Chatrathi Sri Lakshmi Kaumudi, Chintalapani Swamedha Reddy, Sukkarathi Sushmitha, Singireddy Madhavi, Shameerpeta Manasa

International Conference on Educational Technology, Language and Technical Communication (ETLTC)ジャーナル2026-11-05#AI×ESG経営インパクト: 調達リスク対象セクター: cross_sector
DOI: 10.3390/engproc2026143091
原典: https://doi.org/10.3390/engproc2026143091

🤖 gxceed AI 要約

日本語

AI・ML・NLP・ビッグデータがESG報告に組み込まれることで、効率化と同時にAI生成開示がもっともらしく見えつつ不完全・誤導的な情報を含む「グリーンウォッシュ2.0」のリスクが生じる。本論文はAI活用ESG報告をデジタル化・自動化、分析・診断、エージェント型・予測型ESGの3段階で概念化し、データ来歴・人間検証・監査可能性・開示責任・内部通報・規制監督を結ぶ統治フレームワークを提案する。既存の開示枠組みは人間作成の報告を前提としており、AIが生成・変換・評価する際の分散した責任に対応できていないと論じる。

English

As AI, ML, NLP and big data enter ESG reporting, AI-generated disclosures can appear credible while containing incomplete or misleading sustainability information—termed Greenwashing 2.0. The paper conceptualizes AI-enabled ESG reporting across three stages (digitization/automation, analysis/diagnosis, agentic/predictive ESG) and proposes a governance framework linking data provenance, human verification, auditability, disclosure accountability, whistleblowing and regulatory oversight. It argues existing disclosure regimes assume human-authored reporting and fail to address distributed responsibility when AI generates or evaluates sustainability information.

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

SSBJ基準・有報開示・統合報告書でAI活用が進む日本企業にとって、AI生成開示の検証可能性と責任所在をどう設計するかは喫緊の課題。本フレームワークは国内の開示統制・内部統制整備の議論に直接示唆を与える。

In the global GX context

Speaks directly to ISSB/CSRD/SEC climate disclosure debates where assurance and auditability of AI-assisted reporting are unresolved. Offers a governance lens for regulators and standard-setters grappling with distributed accountability in automated sustainability disclosure.

👥 読者別の含意

🔬研究者:AI×ESG開示における検証・帰属・責任の研究課題を体系化する概念的基盤を提供する。

🏢実務担当者:AI生成のESGデータに対する来歴管理・人間検証・監査可能性の統制設計に活用できる。

🏛政策担当者:AI生成開示に対応する規制・執行の枠組み設計の参考になる。

📄 Abstract（原文）

The increasing integration of artificial intelligence (AI), machine learning (ML), natural language processing (NLP), big data analytics, and automated decision-support systems into environmental, social, and governance (ESG) reporting is transforming how sustainability information is collected, analyzed, generated, and communicated. While these technologies can improve reporting efficiency, predictive analytics, real-time monitoring, and data processing, they also create new risks when AI-generated disclosures appear credible while containing incomplete, unsupported, or misleading sustainability information. This paper examines this emerging phenomenon as Greenwashing 2.0, focusing on the role of AI-enabled ESG reporting systems in creating new challenges for verification, traceability, attribution, and regulatory accountability. The study employs a doctrinal and comparative legal methodology supported by analysis of AI technologies, ESG reporting processes, regulatory frameworks, enforcement actions, and documented corporate cases across multiple jurisdictions. The paper conceptualizes AI-enabled ESG reporting through three stages: digitization and automation, analysis and diagnosis, and agentic and predictive ESG. Building on this analysis, it proposes an AI-Enabled ESG Reporting Governance Framework that connects AI system characteristics with data provenance, human verification, auditability, disclosure accountability, whistleblower mechanisms, and regulatory oversight. The analysis demonstrates that existing ESG disclosure frameworks remain substantially oriented toward human-authored reporting and do not adequately address the distributed responsibility created when AI systems generate, transform, or evaluate sustainability information. The paper argues that effective governance of Greenwashing 2.0 requires system-level transparency, traceable data and generation processes, human oversight, AI-specific accountability mechanisms, and risk-calibrated enforcement. The proposed framework provides a basis for integrating Computer Science perspectives on AI-enabled reporting systems with ESG and disclosure regulation.

🔗 Provenance — このレコードを発見したソース

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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。