Explainable AI and Automated Tax Audits: Implications for Taxpayer Trust, Legal Accountability, and Ethical Compliance

The increasing adoption of artificial intelligence (AI) in tax administration has transformed audit selection, risk assessment, and compliance monitoring. While AI-driven tax audits offer efficiency and accuracy, their opaque decision-making processes raise concerns related to taxpayer trust, legal accountability, and ethical compliance. This study examines the role of explainable artificial intelligence (XAI) in addressing these challenges within automated tax audit systems. Using a conceptual and analytical framework grounded in trust theory, procedural justice, and ethical AI principles, the research explores how explainability influences taxpayers’ perceptions of fairness and transparency, enhances the legal defensibility of automated decisions, and mitigates ethical risks such as bias and discrimination. The study finds that explainable AI significantly strengthens taxpayer trust by reducing informational asymmetry and enabling meaningful understanding of audit decisions. It further demonstrates that XAI contributes to legal accountability by supporting reviewability, justification, and contestability of automated assessments. From an ethical perspective, explainability emerges as a critical governance mechanism that promotes fairness and responsible AI deployment in taxation. The study offers practical recommendations for tax authorities, policymakers, and tax professionals, emphasizing the integration of explainability as a core requirement in AI-based tax systems. The findings underscore that the effectiveness of AI in tax administration depends not only on automation but also on transparency, accountability, and ethical alignment.