Explainable Artificial Intelligence and Data Governance for Financial Reporting Quality, Regulatory Accountability and Decision Transparency in United States Organizations

With the increasing use of artificial intelligence (AI) in financial reporting activities, the key concerns have been transparency, accountability, and decision auditability in the United States (US) organizations. The paper examines the effects of Explainable Artificial Intelligence (XAI) and data governance on the quality of financial reporting (FRQ), regulatory accountability (RA) and the decision transparency (DT) of US companies. This study was based on the Agency Theory, Institutional Theory and the Technology Acceptance Model (TAM) and adopted a mixed-methodology which consisted of a structured questionnaire survey of 312 financial professionals working in six industries and a secondary analysis of regulatory filings. The research hypotheses evaluated using Structural Equation Modeling (SEM), multiple regression analysis, mediation and moderation analyses included 9 research hypotheses. The results showed that the quality of financial reporting (0.487, p < 0.001), decision transparency (0.513, p < 0.001), and the quality of data governance (0.421, p < 0.001) are positively associated with XAI adoption. Along these lines, the relationship between XAI and data governance had synergistic impacts on FRQ (0.241, p= 0.01), which were larger than the respective impacts. The total model had an interpretation of 73.1 percent of the financial reporting quality variance. Practical implications are suggestions on XAI-based governance frameworks that are compliant with SEC AI disclosure regulations, PCAOB auditing standards, and NIST AI Risk Management Framework. The research adds new theoretical knowledge of the use of institutional transparency mechanisms in AI-enhanced financial ecosystems and provides a replicable XAI-DG governance model to US organizational settings.