DFAS-ForensicGov: A Doctrinal Framework for Ethical, Explainable, and Sovereign-Sensitive AI Governance in Forensic Accounting

Abstract

Artificial intelligence (AI) is increasingly used in forensic accounting, but AI-generated outputs can create explainability, accountability, evidence-integrity, and cross-border data-governance risks. This study uses a structured conceptual analysis of forensic-accounting literature, AI-governance and AI-auditing research, professional auditing standards, and institutional governance frameworks. The analysis identifies an operational governance gap between general AI governance principles and the requirements for governing AI-generated forensic outputs before they influence consequential institutional decisions. DFAS-ForensicGov is an integrated architecture comprising the Alaali Forensic Governance Doctrine, Alaali Forensic Explainability Escalation Framework, Alaali Forensic Override Command Chain, Alaali Evidence Integrity Calibration Engine, and Alaali Forensic Integrity Ledger. The doctrine establishes a governance sequence in which evidence-pathway conditions are assessed before and throughout AI-assisted processing, while consequential use is governed by explainability, escalation, authorized human intervention, and traceable documentation. It positions AI outputs as investigative inputs requiring accountable human judgment rather than self-validating evidence. The framework is conceptual and has not yet been empirically validated. Future pilot implementation and comparative testing must validate its practical effectiveness.