Globally, taxes are the unarguable lifeblood of a government body, instrumental in providing essential revenue to fund public welfare programs, building and maintaining critical infrastructure, and social welfare programs critical to societal stability and progress.[1] For decades, the public tax administration relied on human tax auditors to review returns, conduct interviews, and apply judgment within legal boundaries[2]. The fast-paced adoption of artificial intelligence (AI) in public tax administration resulted in unprecedented efficiency in revenue collection, fraud detection, and compliance monitoring [3][4]. A fast-changing department with some level of resistance to the change—from traditional human-led audits to algorithm-driven decision systems—it also raises profound ethical questions [5]. This article examines the transition through three lenses: fairness and bias, transparency and accountability, and privacy versus surveillance. Drawing on a few global case studies from the Netherlands, Canada, and India [6][7][8], this paper argues that while AI can bring efficiencies via reducing administrative costs and close taxation gaps and targets, unanswered ethical risks threaten public trust and democratic legitimacy [9]. Artificial intelligence (AI) promises transformative efficiency in tax administration, yet its deployment risks amplifying bias, eroding privacy, and undermining public trust if not guided by rigorous ethical safeguards. This paper proposes a policy framework rooted in human-centered values, fairness, transparency, robustness, and accountability—aligned with ISO/IEC 42001:2023 [11] and ISO/IEC 22989:2022 [12]to ensure AI serves taxpayers equitably while enhancing compliance and operational integrity. [11][12] Drawing on U.S. federal findings, international standards, and practical tools such as AI impact assessments, threat modeling (e.g., STRIDE), and the TRUST principles (Fairness, Accountability, Transparency, Privacy, Inclusivity), the framework outlines a lifecycle-based governance model tailored to tax contexts. [13][14] Key recommendations include mandatory bias audits, human-in-the-loop oversight for high-stakes decisions, public model registries, and regulatory sandboxes for low-risk testing. [15][16] An implementation roadmap with phased milestones and measurable KPIs demonstrates feasibility, illustrated through global benchmarks from Sweden, Australia, and Brazil. [17][18][19] By embedding these principles, tax authorities can harness AI’s potential to reduce administrative burdens, minimize disparate impacts, and foster societal trust in digital governance.

AI Tax administration AI Engineering Machine learning AI Governance AI life Cycle AI Threat Analysis ISO standards for AI governance Cloud Tools for AI governance AI Risks Assessments ISO 42001 ISO 31000 AI Bias Mitigation AI Inspection AI Transparency AI Security and Privacy AI Regulation and Legislations AI Operation and Monitoring AI System Retirement Stride Dread OWASP security for AI AI Risk Identification.