Responsible Artificial Intelligence in Government Fiscal Frameworks: An Interdisciplinary Analysis

Abstract

The integration of Artificial Intelligence (AI) into government fiscal frameworks has emerged as a transformative force, reshaping public financial management, taxation systems, expenditure monitoring, and policy decision-making processes. However, this rapid adoption introduces critical concerns surrounding ethical governance, accountability, transparency, and systemic risk. This study presents an interdisciplinary analysis of responsible AI implementation within government fiscal systems, emphasizing the intersection of technology, ethics, public administration, and risk governance.

The research critically examines existing theoretical and practical approaches to responsible AI, drawing upon established frameworks such as the Assessment List for Trustworthy Artificial Intelligence (ALTAI) and contemporary analyses of societal-scale AI risks. It explores the structural challenges associated with embedding ethical AI principles in public financial systems, including algorithmic bias, opacity, institutional resistance, and regulatory fragmentation. The study further integrates insights from public sector AI applications and global political dynamics influencing AI governance.

A conceptual framework is developed to illustrate how responsible AI can be operationalized within fiscal systems through governance mechanisms, accountability structures, and risk mitigation strategies. The paper also evaluates catastrophic and systemic risks associated with AI misuse in financial governance, emphasizing the importance of anticipatory policy design and interdisciplinary oversight.

Findings indicate that while AI enhances efficiency, predictive accuracy, and decision-making capabilities in fiscal management, its unregulated or poorly governed deployment can exacerbate inequalities, reduce institutional trust, and introduce large-scale systemic vulnerabilities. Ethical AI adoption in fiscal frameworks requires a balance between technological innovation and normative governance principles.

The study contributes to academic and policy discourse by proposing an integrated model for responsible AI governance tailored to public financial systems. It highlights the necessity of cross-sector collaboration, continuous risk assessment, and adaptive regulatory mechanisms to ensure that AI-driven fiscal governance remains equitable, transparent, and resilient in the long term.