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Artificial Intelligence In GST Compliance: Balancing Technological Innovation And Taxpayer Rights Under The Indian Indirect Tax Regime




Nandini Sharma, Symbiosis Law School, Nagpur


ABSTRACT


The last five years have seen huge disruptions and rapid advancements in many fields due to the introduction and rising popularity of Artificial Intelligence (AI). Tax Compliance is a field that has also been at the centre of these advancements. AI brings opportunities for automated invoice processing, risk and fraud detection, and data-driven models for tax administration. In India, the digitization of the Goods and Services Tax (GST) framework, combined with the rapid advancements in AI, will add to the overall efficiency of tax compliance and make the collection of GST more effective. However, AI's disruptive nature means that Adverse Impacts of AI - like lack of accountability, and data processing and privacy issues - will also be part of the tax compliance framework.


This study seeks to address the benefits and disruptive nature of AI in the tax compliance framework and, more specifically, the adverse impacts of AI on the rights of taxpayers - especially on the transparency and accountability of automated decisions. A recommended framework will include approaches from other jurisdictions. The study will focus on the objectives of the Central Goods and Services Tax Act, 2017, the rules pertaining to the Act, and related policy changes.


The paper will recommend the implementation of AI in the Indian GST Tax Compliance System to greatly improve compliance and administrative efficiency, justified within the framework of taxpayers’ rights.



Indian Journal of Law and Legal Research

Abbreviation: IJLLR

ISSN: 2582-8878

Website: www.ijllr.com

Accessibility: Open Access

License: Creative Commons 4.0

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Licensing: 

 

All research articles published in The Indian Journal of Law and Legal Research are fully open access. i.e. immediately freely available to read, download and share. Articles are published under the terms of a Creative Commons license which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

 

Disclaimer:

The opinions expressed in this publication are those of the authors. They do not purport to reflect the opinions or views of the IJLLR or its members. The designations employed in this publication and the presentation of material therein do not imply the expression of any opinion whatsoever on the part of the IJLLR.

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