AIによる持続可能性報告の透明性と説明責任の実現
AI-Enabled transparency and accountability in sustainability reporting (原題)
Kjartan Sigurðsson
🤖 gxceed AI 要約
日本語
本書は、AIが持続可能性報告（特にESG報告）を制度的変革へ導く可能性を分析。ブロックチェーン、生成AI、リアルタイム監視がデータの生成・検証・解釈を変革し、透明性を定期開示から組み込み型のデジタル基盤へ移行させると論じる。ガバナンス成熟度、組織能力、規制整合が不可欠で、解釈可能性や責任拡散などの構造的緊張も指摘。
English
This chapter analyzes AI's transformative role in sustainability reporting, particularly ESG, arguing it represents institutional change rather than mere technological improvement. It examines how blockchain, generative AI, and real-time monitoring shift transparency from periodic disclosure to embedded digital infrastructure. AI can enhance ESG performance and disclosure consistency only with governance maturity and regulatory alignment, while facing tensions like interpretability and responsibility diffusion.
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開示や有報でのサステナビリティ情報拡充が進む日本では、AI活用による開示の効率化と信頼性向上が実務課題。本稿の制度的視点は、日本企業がAIを単なる効率化ツールでなくガバナンス変革として捉える示唆を与える。
In the global GX context
As ISSB and CSRD reshape global disclosure, this chapter offers a governance lens on AI-driven reporting infrastructure. It highlights that AI's benefits depend on institutional embedding, relevant for regulators and firms building trustworthy transparency systems.
👥 読者別の含意
🔬研究者:AIとESG報告の制度的相互作用を理論的に整理した枠組みを提供。
🏢実務担当者:AI導入を開示戦略に組み込む際のガバナンス要件とリスクを理解できる。
🏛政策担当者:AI時代の開示規制設計における透明性と説明責任の課題を示唆。
📄 Abstract（原文）
This chapter explores the prospective role of artificial intelligence (AI) in sustainability reporting, with particular attention to environmental, social, and governance (ESG) reporting, arguing that AI-enabled transparency represents an institutional transformation rather than a simple technological improvement. It analyses how blockchain, generative AI, and real-time monitoring systems are reshaping the production, verification, and interpretation of sustainability data, shifting transparency from periodic disclosure to embedded digital infrastructure. Drawing on institutional theory, governance literature, and recent empirical studies, the chapter demonstrates that AI can improve environmental, social, and governance performance, disclosure consistency, and risk responsiveness, but only when governance maturity, organisational capability, and regulatory alignment are present. It further identifies structural tensions, such as interpretability challenges, epistemic capture, synthetic legitimacy, and responsibility diffusion. The chapter concludes that the future of AI-driven sustainability reporting will depend more on the governance and institutional embedding of transparency infrastructures than on technical sophistication.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.4324/9781003747567-7first seen 2026-08-20 04:48:58
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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。