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Articles

AI Integration in Government Mobile Platforms for Secure and Innovative Digital Solutions

Mahendar Ramidi

Independent Researcher, USA

Abstract

Artificial intelligence (AI) is rapidly transforming the aspect of government mobile and is causing the improvement of the identity verification process, document processes, personalization, and service delivery. This paper looks at ways of introducing AI in government mobile applications safely and remaining open on issues of privacy and compliance. It ponders over such significant architectural designs as on-device AI inference, cloud- assisted intelligence, and human-in-the-loop decision-making framework. What is more, the study focuses on the importance of explainable AI methods to ensure the digital services are trusted and held accountable by the public. The suggested framework demonstrates how AI-enhanced mobile platforms may improve the efficiency of the operations, improve communication with citizens, and improve the accuracy of services, whereas reducing privacy and security risks. Through the analysis of the implementation of secure AI solutions, the paper brings out the prospects of AI to transform the services in the public sector so that the implementation of AI does not affect governance, privacy, and the legal provisions

Article Information

Journal

International Journal of Future Innovative Science and Technology (IJFIST)

Volume (Issue)

Vol. 8 No. 2 (2025): International Journal of Future Innovative Science and Technology (IJFIST)

DOI

https://doi.org/10.15662/IJFIST.2025.0802002

Pages

14532-14543

Published

March 7, 2025
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Mahendar Ramidi (2025). AI Integration in Government Mobile Platforms for Secure and Innovative Digital Solutions. International Journal of Future Innovative Science and Technology (IJFIST) , Vol. 8 No. 2 (2025): International Journal of Future Innovative Science and Technology (IJFIST) , pp. 14532-14543. https://doi.org/10.15662/IJFIST.2025.0802002

References

[1] P.K. Agarwal, "Public administration challenges in the world of AI and bots," Public Administration Review, vol. 78, no. 6, pp. 917–921, 2018.
[2] R. Madan and M. Ashok, "AI adoption and diffusion in public administration: a systematic literature review and future research agenda," Gov. Inf. Quart., vol. 40, p. 101774, 2022.
[3] R. Medaglia, J.R. Gil-Garcia, and T.A. Pardo, "Artificial intelligence in government: taking stock and moving forward," Soc. Sci. Comput. Rev., vol. 41, no. 1, pp. 123–140, 2023.
[4] B.W. Wirtz and W.M. Müller, "An integrated artificial intelligence framework for public management," Pub. Manag. Rev., vol. 21, no. 7, pp. 1076–1100, 2019.
[5] M.M. Young, J.B. Bullock, and J.D. Lecy, "Artificial discretion as a tool of governance: a framework for understanding the impact of artificial intelligence on public administration," Perspect. Public Manage. Governance, vol. 2, no. 4, pp. 301–313, 2019.
[6] S. Zouridis, M. Van Eck, and M. Bovens, "Automated discretion," in Discretion and the Quest for Controlled Freedom, T. Evans and P. Hupe, Eds., Cham: Springer Int. Publ., 2020, pp. 313–329.
[7] B.W. Wirtz, J.C. Weyerer, and C. Geyer, "Artificial intelligence and the public sector—applications and challenges," Int. J. Public Admin., vol. 42, no. 7, pp. 596–615, 2019.
[8] M. Kuziemski and G. Misuraca, "AI governance in the public sector: three tales from the frontiers of automated decision-making in democratic settings," Telecommunications Policy, vol. 44, no. 6, p. 101976, 2020.
[9] J.I. Criado and J.R. Gil-Garcia, "Creating public value through smart technologies and strategies: From digital services to artificial intelligence and beyond," Int. J. Public Sect. Manag., vol. 32, no. 3, pp. 217–236, 2019.
[10] M. Ashok, R. Madan, A. Joha, and U. Sivarajah, "Ethical framework for artificial intelligence and digital technologies," Int. J. Inf. Manage., vol. 62, p. 102433, 2022.
[11] D. Teja Santosh et al., "Development of IoT based intelligent irrigation system using particle swarm optimization and XGBoost techniques," Bull. Electr. Eng. Informatics, vol. 13, no. 3, pp. 1927–1934, Jun. 2024, doi: 10.11591/eei.v13i3.6332.
[12] R. Mahyudin, D.T. Sanchez, S.T. Mrudula, R.K. Veluri, and J. Malik, "Design of Automated Smart Attendance System Using Deep Learning Based Face Recognition," in Proc. 9th Int. Conf. on Sci. Technol. Eng. Math. (ICONSTEM), Chennai, India, 2024, pp. 1–6, doi: 10.1109/ICONSTEM60960.2024.10568825.