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All Journal Journal of Applied Taxation and Policy Journal of Innovative Technologies in Learning and Education
Kamal Singh Kunwar
Tribhuvan University
Computer Science & IT Economics, Econometrics & Finance Education Social Sciences Other

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Algorithmic Tax Governance and Compliance Behavior: Evaluating the Impact of Digital Tax Systems on Fiscal Capacity and Public Trust in Developing Economies Kamal Singh Kunwar
Journal of Applied Taxation and Policy Volume 2, Issue 2 (November) 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jatap.v2i1.411

Abstract

This study develops a theoretically grounded conceptual framework to examine how algorithmic tax governance defined as the integration of artificial intelligence and digital infrastructures into tax administration reshapes taxpayer compliance behavior, state fiscal capacity, and public trust in developing country contexts. It addresses persistent inefficiencies in conventional tax systems alongside the uneven consequences of ongoing digital transformation. Employing a theory-building and integrative conceptual approach, the study synthesizes interdisciplinary scholarship (2020–2025) from institutional theory, behavioral economics, and fiscal sociology. It proposes a multi-layered analytical model capturing the dynamic interaction among technological systems, governance structures, and taxpayer decision-making processes. The analysis identifies three principal pathways through which algorithmic governance affects compliance: (1) the restructuring of taxpayer cognition through enhanced data-driven monitoring and perceived detection probability; (2) the signaling of administrative competence and procedural fairness via digital interfaces; and (3) the reinforcement of enforcement capacity through automated risk assessment mechanisms. However, these effects remain contingent upon institutional credibility, transparency, and equitable digital access, with deficiencies in these conditions potentially constraining compliance outcomes. The study advances the “Trust–Technology–Compliance Nexus” as an integrative framework that conceptually links digital governance, compliance behavior, and fiscal performance, while addressing fragmentation in existing literature. As a conceptual contribution, the framework is not empirically tested but is intended to guide future research and policy design. The findings emphasize the need for transparent, accountable, and inclusive digital tax systems to strengthen voluntary compliance and enhance fiscal legitimacy in developing economies.
Federated Explainable Multi-Agent Governance Framework for Adaptive Personalized Learning Equity in Higher Education KAMAL SINGH KUNWAR
Journal of Innovative Technologies in Learning and Education Vol. 3 No. 2 (2026): Journal of Innovative Technologies in Learning and Education - September 2026
Publisher : Universitas Citra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37792/rxtajs29

Abstract

The rapid deployment of artificial intelligence (AI) in higher education raises questions of equity, transparency, accountability, privacy, and governance in adaptive personalized learning. While AI-enabled personalization can increase educational responsiveness, top-down personalized learning architectures may reinforce inequality through decentralized institutional data representation, esoteric decision-making, and insufficient stakeholder agency. Prior work has advanced federated learning, multi-agent systems, explainable AI (XAI), and algorithmic fairness, yet such components have not been fully integrated into a governance architecture for equitable higher education. This paper proposes an adaptive personalized learning federated explainable multi-agent governance architecture for equitable higher education. A theory-building research methodology leveraging critical realism and abductive inference combines concepts in distributed machine learning, multi-agent coordination, XAI, educational equity, and AI governance. The architecture consists of four interacting layers: federated learning for distributed institutional data; multi-agent governance comprising student, instructor, policy, and optimizer agents; explainability for decision traceability and interpretation; and equity evaluation to inform ongoing bias mitigation. Theoretical triangulation, construct-to-function mapping, cross-domain consistency testing, and boundary-condition analysis serve as the analytical validation process. Privacy, distributed governance, XAI, and equity are systemically positioned as mutually constraining properties. The architecture can inform design principles and empirical evaluation for agency-aware, privacy-preserving, equitable institutional AI governance.
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