TITLE:
Zero-Base Budgeting and Artificial Intelligence Applied to the Brazilian Public Sector
AUTHORS:
Licurgo J. Mourão, Luciano Vieira de Araújo, Ana Carla Bliacheriene
KEYWORDS:
Artificial Intelligence, Zero-Based Budgeting, Algorithm Governance, Public Budget, Accountability, Explainability (XAI)
JOURNAL NAME:
Open Journal of Social Sciences,
Vol.14 No.8,
August
27,
2026
ABSTRACT: This paper assesses the intersection between Zero-Based Budgeting (ZBB) and Artificial Intelligence (AI) in the context of the Brazilian public sector, emphasizing their combined potential to increase efficiency, transparency, and rationality in the allocation of public resources. While ZBB demands full justification of each expenditure from a zero base, breaking with the inertia of incremental budgeting, AI provides the analytical and computational infrastructure necessary to support such scrutiny through data-driven modeling and predictive analytics. The incorporation of intelligent technologies—such as machine learning, big data, and blockchain—into budgetary processes allows for the automation of information collection and processing, the identification of inefficiencies, and the support of evidence-based public decisions, aligning fiscal choices with measurable social outcomes. This study adopts an analytical and interdisciplinary perspective, exploring the opportunities and challenges inherent in the Brazilian institutional context, characterized by normative complexity, bureaucratic rigidity, and technological asymmetries. It argues that AI-assisted Zero-Based Budgeting (ZBB) is a transformative mechanism to redefine the logic of fiscal governance, promoting greater coherence between public policies, fiscal responsibility, and the constitutional principles of efficiency and administrative morality. Ultimately, the convergence of these two dimensions represents more than a managerial reform: it is a paradigmatic shift towards a strategic, data-driven, and ethically conscious model of public administration, able to align technological rationality with democratic objectives and national development. Furthermore, this paper focuses on the critical and dogmatic analysis of artificial intelligence (AI) in the context of public budgeting in Brazil. The central assumption is that the introduction of predictive automation into the budgetary cycle inaugurates a profound paradigmatic shift, moving the axis of decisional rationality from legal norms and political deliberation to statistical inference and mass data. Under the aegis of a theoretical-doctrinal and interdisciplinary approach, which articulates Artificial Intelligence, Financial Law, Administrative Law, and public ethics, the work examines the challenges posed by algorithmic incommensurability and the risk of budgetary discrimination, confronting the pursuit of technical efficiency with the imperative of democratic legitimacy. The hypothesis is that the harmonious coexistence between technological innovation and the public budget is only perfected through the establishment of a robust algorithmic governance system, anchored in the pillars of explainability (XAI), public auditing, and non-delegable institutional responsibility. Finally, a legal and institutional framework for budgetary AI in Brazil is proposed, aiming to safeguard the constitutional principles of legality, transparency, and administrative morality.