A Review of Artificial Intelligence in Government and its Potential from a Public Policy Perspective
Open accessAbstract
Artificial intelligence (AI) is the latest trend being implemented in the public sector. Recent advances in this field and the AI explosion in the private sector have served to promote a revolution for government, public service management, accountability, and public value. Incipient research to understand, conceptualize and express challenges and limitations is now ongoing. This paper is the first approach in such a direction; our research question is: What are the current AI trends in the public sector? In order to achieve that goal, we collected 78 papers related to this new field in recent years. We also used a public policy framework to identify future areas of implementation for this trend. We found that only normative and exploratory papers have been published so far and there are a lot of public policy challenges facing in this area, and that AI implementation results are unknown and unexpected; since there may be great benefits for governments and society, but, on the other hand, it may have negative results like the so-called ”algorithmic bias” of AI when making important decisions for social development. However, we consider that AI has potential benefits in the public health, public policies on climate change, public management, decision-making, disaster prevention and response, improving government-citizen interaction, personalization of services, interoperability, analyzing large amounts of data, detecting abnormalities and patterns, and discovering new solution…
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Any policy interventions based on AI models will lack statistically significant perspectives on such clusters and thereby further exasperating the digital divide.
“Any policy interventions based on AI models will lack statistically significant perspectives on such clusters and thereby further exasperating the digital divide”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as D. Valle-Cruz et al., 2019
Cites
Artificial Intelligence and the Public Sector—Applications and Challenges
Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019) · International Journal of Public Administration · doi:10.1080/01900692.2018.1498103 · Cited by 1,341 · cited 4× · see citing works
“Our research question was:What are the current trends in AI in the public sector? Using the categories developed by Wirtz [33]: AI government service; AI public order and law; AI ethics, and AI government policy, our research focused on the latter.”
BackgroundInfluentialSun, T. Q., & Medaglia, R. (2019) · Government Information Quarterly · doi:10.1016/j.giq.2018.09.008 · Cited by 948 · see citing works
Big data in the policy cycle: Policy decision making in the digital era
Höchtl, J., Parycek, P., & Schöllhammer, R. (2016) · Journal of Organizational Computing and Electronic Commerce · doi:10.1080/10919392.2015.1125187 · Cited by 331 · cited 2× · see citing works
“Some authors suggest that evaluation using data and AI will transform the notion of evaluation itself [12].”
BackgroundMethodBig data and AI - A transformational shift for government: So, what next for research?
Pencheva, I., Esteve, M., & Mikhaylov, S. J. (2020) · Public Policy and Administration · doi:10.1177/0952076718780537 · Cited by 272 · see citing works
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Zuiderwijk, A., Chen, Y.-C., & Salem, F. (2021) · Government Information Quarterly · doi:10.1016/j.giq.2021.101577 · Cited by 795 · see citing works
When combinations of humans and AI are useful: A systematic review and meta-analysis
Vaccaro, M., Almaatouq, A., & Malone, T. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02024-1 · Cited by 570 · see citing works
Madan, R., & Ashok, M. (2023) · Government Information Quarterly · doi:10.1016/j.giq.2022.101774 · Cited by 468 · see citing works
Artificial Intelligence in Government: Taking Stock and Moving Forward
Medaglia, R., Gil-Garcia, J. R., & Pardo, T. A. (2023) · Social Science Computer Review · doi:10.1177/08944393211034087 · Cited by 159 · see citing works
Yigitcanlar, T., Li, R. Y. M., Beeramoole, P. B., & Paz, A. (2023) · Government Information Quarterly · doi:10.1016/j.giq.2023.101833 · Cited by 142 · see citing works
Truth or dare?–how can we influence the adoption of artificial intelligence in municipalities?
Schaefer, C., Lemmer, K., Samy Kret, K., Ylinen, M., Mikalef, P., & Niehaves, B. (2021) · Proceedings of the Annual Hawaii International Conference on System Sciences · doi:10.24251/hicss.2021.286 · Cited by 63 · see citing works
Governing Ethical AI Transformation: A Case Study of AuroraAI
Leikas, J., Johri, A., Latvanen, M., Wessberg, N., & Hahto, A. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.836557 · Cited by 37 · see citing works
The strategy of develop Artificial Intelligence in singapore, united states, and united kingdom
Zahra, A. A., & Nurmandi, A. (2021) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/717/1/012012 · Cited by 22 · see citing works
- Authors
- Valle-Cruz, D., Alejandro Ruvalcaba-Gomez, E., Sandoval-Almazan, R., & Ignacio Criado, J.
- Affiliations
- Universidad
- ORCID
- Valle-Cruz, David
- Venue
- Proceedings of the 20th Annual International Conference on Digital Government Research
- Publisher
- ACM
- Published
- 2019-06-18
- Pages
- 91-99
- ISSN
- 3050-8681 (ISSN-L)
- OpenAlex
- W2950647473
- Cited by
- 149
- References
- 4
- Citations by year
- 2026: 14 · 2025: 46 · 2024: 25 · 2023: 24 · 2022: 20 · 2021: 8 · 2020: 10 · 2019: 2
- FWCI
- 68.92
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Annual International Conference on Digital Government Research (publishedVersion)
- Topics
- Social Sciences › Political Science and International Relations › E-Government and Public Services
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Public Administration › Public Policy and Administration Research
- Keywords
- artificial intelligence in government, public sector AI, public policy challenges, algorithmic bias, public service management, government accountability, public value, AI implementation, disaster prevention and response, government-citizen interaction, climate policy, interoperability, data analysis
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