A Study on Enhancing Government Efficiency and Public Trust: The Transformative Role of Artificial Intelligence and Large Language Models
Description
This paper examines the transformative potential of Artificial Intelligence (AI), specifically Large Language Models (LLMs), in enhancing government efficiency and public sector service delivery. By integrating AI into various governmental functions such as automated administrative tasks, public safety, resource management, citizen services, policy development, and fraud detection, governments worldwide can significantly streamline operations, improve decision-making, and enhance citizen engagement. Detailed potential case studies from the United States’ IRS and local government agencies like SSA illustrate the successful implementation of AI, demonstrating its substantial benefits in operational efficiency and public satisfaction. The study concludes with strategic recommendations for further AI adoption, emphasizing the importance of robust governance, continuous technological investment, workforce training, and maintaining public trust. This research underscores AI's critical role in modernizing government functions and fostering a more responsive and inclusive public service landscape.
Files
IJEMR2024140310.pdf
Files
(246.9 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:938725597e9bd712650999150e8da6ba
|
246.9 kB | Preview Download |
Additional details
References
- Kersbergen, K.V. & Waarden, F.V. (2004), 'Governance' as a bridge between disciplines: Cross-disciplinary inspiration regarding shifts in governance and problems of governability, accountability and legitimacy. European Journal of Political Research, 43, 143-171. DOI: 10.1111/j.1475-6765.2004.00149.x.
- Osborne, S. P. (2006). The new public governance?. Public Management Review, 8(3), 377–387. DOI: 10.1080/14719030600853022.
- Mo, Y., et al. (2024). LLM AI text generation detection based on Transformer deep learning algorithm. International Journal of Engineering and Management Research, 14(2), 154-159.
- Daly, Angela, et al. (2019). Artificial Intelligence Governance and Ethics: Global Perspectives. arXiv preprint arXiv:1907.03848.
- Liu, J., et al. (2024). Unraveling large language models: From evolution to ethical implications. World Scientific Research Journal, 10(5), 97-102. DOI: 10.6911/WSRJ.202405_10(5).0012.
- Zhao, W., Liu, X., Xu, R., Xiao, L. & Li, M. (2024). E-commerce webpage recommendation scheme base on semantic mining and neural networks. Journal of Theory and Practice of Engineering Science, 4(03), 207–215. https://doi.org/10.53469/jtpes.2024.04(03).20.
- Lin, Z., et al. (2024). Text sentiment detection and classification based on integrated learning algorithm. Applied Science & Engineering Journal for Advanced Research, 3(3), 27-33.