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

Abstract

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.