ARTIFICIAL INTELLIGENCE AND STAKEHOLDER-DRIVEN GST GOVERNANCE: ADVANCING SUSTAINABLE ECONOMIC DEVELOPMENT AND EVIDENCE-BASED PUBLIC POLICY
Keywords:
Artificial Intelligence, GST Governance, Tax Compliance, Sustainable Economic Development, Evidence-Based Public Policy, Digital Tax Administration.,,Abstract
The growing complexity of the Goods and Services Tax (GST) framework in India
has created a need for technologically advanced systems to improve compliance, transparency,
and administrative efficiency. Artificial Intelligence (AI) has emerged as a transformative tool
capable of strengthening tax governance through automation, data analytics, and intelligent
decision-making. This study examines the role of AI in promoting stakeholder-driven GST
governance and its potential to advance sustainable economic development and evidence-based
public policy. It explores the application of AI technologies such as Optical Character
Recognition (OCR), Natural Language Processing (NLP), Machine Learning (ML), Robotic
Process Automation (RPA), chatbots, blockchain, and Facial Recognition Technology (FRT) in
improving GST compliance, fraud detection, and tax administration. The findings indicate that
AI-driven systems can enhance reporting accuracy, reduce compliance burdens, and support
real-time monitoring of tax transactions. However, challenges relating to data privacy,
regulatory oversight, and algorithmic accountability remain significant. The study concludes
that responsible and well-regulated AI integration in GST governance can strengthen fiscal
transparency, institutional efficiency, and sustainable economic development.
References
Association for the Advancement of Artificial Intelligence. (n.d.). About artificial
intelligence. https://www.aaai.org
Brundtland Commission. (1987). Our common future. Oxford University Press.
Hunt, E. B. (2014). Artificial intelligence. Academic Press.
Ministry of Electronics & Information Technology. (2024, March 7). Cabinet approves
ambitious IndiaAI mission to strengthen the AI innovation ecosystem. Press Information
Bureau. Release ID: 2012357.
NITI Aayog. (2021). National strategy for artificial intelligence. Government of India.
NITI Aayog. (2022). Responsible AI for all: Adopting the framework – A use case
approach on facial recognition technology. Government of India.
Reinsel, D., Gantz, J., & Rydning, J. (2018). Data Age 2025: The digitization of the
world from edge to core. International Data Corporation.
Securities and Exchange Board of India. (2019, January 4). Reporting for artificial
intelligence (AI) and machine learning (ML) applications and systems offered and used
by market intermediaries. Circular No. SEBI/HO/MIRSD/DOS2/CIR/P/2019/10.
Sitharaman, N. (2023, April 15). AI-driven tax fraud detection and shell company
tracking. The Times of India. https://timesofindia.indiatimes.com
Sitharaman, N. (2023, October 20). AI-based analysis in economic offenses and tax
evasion tracking. The Hindu. https://www.thehindu.com
United Nations. (2015). Transforming our world: The 2030 agenda for sustainable
development. United Nations.
United Nations. (2023). Seizing the opportunities of safe, secure and trustworthy
artificial intelligence systems for sustainable development. UN General Assembly, 78th
Session.
U.S. Department of State. (2021). Artificial intelligence and foreign policy 2021–2025.
U.S. Government Publishing Office.
Winston, P. H. (1992). Artificial intelligence. Addison-Wesley Longman Publishing.
