Generative AI in Accounting Policies and Estimates: Safeguarding Professional Judgment and Addressing Managerial Bias

Generative artificial intelligence (GenAI) is increasingly used in corporate reporting, where accounting policies and estimates require professional judgment. This conceptual study examines how GenAI may assist preparers without displacing human responsibility or reinforcing managerial bias. Using theory adaptation, it integrates research on professional judgment, motivated reasoning and automation reliance. It identifies five bias-amplification mechanisms: anchoring, automation bias, outcome-directed prompting, unverified justification and diffusion of responsibility. The paper develops TRACE, a proposed framework comprising task boundary, regulatory grounding, alternative assessment, challenge and contradiction, and evidence trail. Its intended operation is illustrated through four constructed IFRS cases covering IAS 8 and IAS 16, IAS 36, IAS 37 and IFRS 15, three involving climate-transition assumptions. The AI proposals are stylised illustrations, not actual model outputs or empirical data. The analysis suggests that GenAI is most defensible when used to identify alternatives and contradictory evidence and to challenge a preferred treatment, and least defensible when asked to select or justify a conclusion. TRACE may make AI-assisted judgments more attributable and contestable, but it cannot guarantee unbiased or technically correct outcomes; independent human review remains necessary. The framework has not been empirically validated. It considers governance, control, reporting reliability and sustainable management.