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Digital Leviathan or Efficient Governance? Navigating the AI Frontier in Indian Income Tax Administration

Author(s) Mrityunjai Singh
Country India
Abstract AI in tax administration is a fundamental shift in the architecture of the modern state. In India, the ‘Faceless Assessment Scheme’, ‘Insight Portal’ and AI-based risk analytics are now a cornerstone of governance and a tool for revenue generation, reducing human arbitrariness, and detecting fraud. But such technological tools, while attractive, also raise fundamental legal and constitutional issues. In this paper, we assess the duality of AI in Indian income tax administration and its potential to become a "Digital Leviathan" (a huge digital entity which can be opaque, surveillance-based and coercive). Based on 'Technology Adoption Theory' and administrative law and the tenets of the Rule of Law, we explore the tension between AI efficiency against values of transparency and accountability, rational decision-making, privacy and natural justice. The article argues that the evaluation of AI in tax administration should not be based solely on revenue collection, compliance rates, and efficiency gains, but also on procedural fairness and taxpayer rights. It cites the risks as algorithmic bias, opaque "black-box" decision-making processes, a volume of data collection and a lack of human involvement in the quasi-judicial framework of tax administration. It should be clear that there is a need for a regulatory framework for AI-enabled tax administration in India, one that provides a "Human-in-the-Loop," or a framework that is technologically advanced and that is also legally sound in terms of data collection and accountability (i.e. auditable, accountable, explainable and responsible). This article has been a significant contribution to the discussion on digital governance, fiscal legitimacy, state power in the online era and state management with respect to digital governance and data.
Published In Volume 2, Issue 2, February 2021
Published On 2021-02-06
DOI https://doi.org/10.70528/IJLRP.v2.i2.2095
Short DOI https://doi.org/hbwh34
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