← 論文一覧に戻る

サステナビリティ保証における人工知能：会計上の課題、監査リスク、ESG検証のための概念的枠組み

Artificial Intelligence in Sustainability Assurance: Accounting Challenges, Audit Risks and a Conceptual Framework for ESG Verification (原題)

Radosveta Krasteva-Hristova, Vanya Georgieva

Accounting and Auditing📚 査読済 / ジャーナル2026-09-01#AI×ESGOrigin: EU経営インパクト: 調達リスク対象セクター: cross_sector
DOI: 10.3390/accountaudit2030015
原典: https://doi.org/10.3390/accountaudit2030015

🤖 gxceed AI 要約

日本語

本論文は、サステナビリティ報告の保証におけるAIの役割を体系的に検討し、AIが付加価値を生む5領域（証拠抽出、基準マッピング、異常・グリーンウォッシュ検出、外部データ照合、文書化支援）を特定する。一方で、データ品質、説明可能性、バイアス、ゲーミング、監査人の過信などのリスクを挙げ、段階的信頼上限と非委任決定を定めた「責任あるAI支援サステナビリティ保証フレームワーク」を提案する。ISSA 5000に基づき国際的に一般化可能なモデルを提示。

English

This conceptual paper systematically examines AI's role in sustainability assurance, identifying five value-adding domains: evidence extraction, criteria mapping, anomaly/greenwashing screening, external-data triangulation, and documentation support. It also outlines risks such as data quality, explainability, bias, gaming, and auditor overreliance, and proposes a Responsible AI-Assisted Sustainability Assurance Framework with graded reliance ceilings and non-delegable decisions. The framework is illustrated in Europe and generalizable through ISSA 5000.

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開示が始まり、保証制度の整備が進む中、AI活用の枠組みは監査実務や内部統制に示唆を与える。特に、有報や統合報告書の保証においてAIの利用範囲とガバナンスを定める際の参考となる。

In the global GX context

Globally, as ISSB and CSRD drive assurance requirements, this framework offers a testable model for integrating AI into assurance under ISSA 5000, addressing audit risk and regulatory expectations.

👥 読者別の含意

🔬研究者:AIと保証の交差領域の研究に、検証可能な枠組みとリスク分類を提供。

🏢実務担当者:保証業務におけるAI利用のガバナンス設計に直接活用できる。

🏛政策担当者:AIを活用した保証の規制枠組みを検討する際の基礎資料となる。

📄 Abstract（原文）

Sustainability reporting is moving from voluntary narrative disclosure toward regulated, evidence-based and externally assured corporate reporting, creating an assurance problem that artificial intelligence (AI) is expected to help address. Because AI is embedded in accounting and audit workflows, its outputs increasingly shape how assurance evidence is located, tested and evaluated. This conceptual article develops an assurance-specific framework answering three questions: for which sustainability-assurance procedures AI creates analytical value, which risks arise when AI influences assurance work, and which decision rights and controls should govern that influence. Integrating assurance standards, accounting and auditing research, AI-governance frameworks and behavioural studies, it finds AI adds value in five domains—evidence extraction, criteria mapping, anomaly and greenwashing screening, external-data triangulation, and documentation support—but only under defined base rates, error costs and source traceability. It identifies the risks limiting reliance: data, source fidelity, explainability, bias, calibration, preparer gaming, and auditor overreliance. The Responsible AI-Assisted Sustainability Assurance Framework sets graded reliance ceilings, non-delegable decisions, calibrated decision gates, anti-gaming safeguards and ex-post metrics, permitting clerical assistance, analytical recommendation and constrained agentic execution while prohibiting autonomous decisions on materiality, evidence sufficiency and conclusions. Illustrated in Europe, it generalises through ISSA 5000 as a testable model.

🔗 Provenance — このレコードを発見したソース

🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。

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。