Ai-Augmented Public Administration: Balancing Innovation with Democratic Values
Abstract
Authors
How to Cite
##plugins.themes.bootstrap3.article.details##
References
Ahn, M. J., & Chen, Y. C. (2022). Digital transformation toward AI-augmented public administration: The perception of government employees and the willingness to use AI in government. Government Information Quarterly, 38(4), 101520. https://doi.org/10.1016/j.giq.2021.101520
Alon-Barkat, S., & Busuioc, M. (2023). Human–AI interactions in public sector decision making: "Automation bias" and "selective adherence" to algorithmic advice. Journal of Public Administration Research and Theory, 33(1), 153-169. https://doi.org/10.1093/jopart/muac007
AmplifAI. (2025). 60+ Generative AI statistics you need to know in 2025. Retrieved from https://www.amplifai.com/blog/generative-ai-statistics
Ananny, M., & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society, 20(3), 973-989. https://doi.org/10.1177/1461444816676645
Aoki, N. (2020). An experimental study of public trust in AI chatbots in the public sector. Government Information Quarterly, 37(4), 101490. https://doi.org/10.1016/j.giq.2020.101490
Ashok, M., & Haleem, A. (2022). AI adoption and diffusion in public administration: A systematic literature review and future research agenda. Government Information Quarterly, 39(4), 101726. https://doi.org/10.1016/j.giq.2022.101726
Bank of China. (2024). Annual report on AI adoption in Chinese financial institutions. Beijing: Bank of China Research Institute.
Bannister, F., & Connolly, R. (2020). Administration by algorithm: A risk management framework. Information Polity, 25(4), 471-490. https://doi.org/10.3233/IP-200275
Barocas, S., & Selbst, A. D. (2016). Big data's disparate impact. California Law Review, 104(3), 671-732. https://doi.org/10.15779/Z38BG31
BCG. (2024). AI adoption in 2024: 74% of companies struggle to achieve and scale value. Boston Consulting Group. Retrieved from https://www.bcg.com/press/24october2024-ai-adoption-in-2024
Beamer, G. (2002). Elite interviews and state politics research. State Politics & Policy Quarterly, 2(1), 86-96. https://doi.org/10.1177/153244000200200106
Belanche, D., Casaló, L. V., Flavián, C., & Schepers, J. (2014). Trust transfer in the continued usage of public e-services. Information & Management, 51(6), 627-640. https://doi.org/10.1016/j.im.2014.05.016
Binns, R. (2018). Algorithmic accountability and public reason. Philosophy & Technology, 31(4), 543-556. https://doi.org/10.1007/s13347-017-0263-5
Bovens, M. (2007). Analysing and assessing accountability: A conceptual framework. European Law Journal, 13(4), 447-468. https://doi.org/10.1111/j.1468-0386.2007.00378.x
Bovens, M., & Zouridis, S. (2002). From street‐level to system‐level bureaucracies: How information and communication technology is transforming administrative discretion and constitutional control. Public Administration Review, 62(2), 174-184. https://doi.org/10.1111/0033-3352.00168
Brookings Institution. (2023). Algorithmic bias detection and mitigation: Best practices and policies to reduce consumer harms. Washington, DC: Brookings Institution Press.
Busuioc, M. (2020). Accountable artificial intelligence: Holding algorithms to account. Public Administration Review, 81(5), 825-836. https://doi.org/10.1111/puar.13293
Chen, T. (2025). Holding AI-based systems accountable in the public sector: A systematic review. Public Performance & Management Review, 48(2), 412-438. https://doi.org/10.1080/15309576.2025.2469784
Chouldechova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big Data, 5(2), 153-163. https://doi.org/10.1089/big.2016.0047
Coglianese, C., & Lehr, D. (2019). Transparency and algorithmic governance. Administrative Law Review, 71(1), 1-56.
Creswell, J. W., & Plano Clark, V. L. (2017). Designing and conducting mixed methods research (3rd ed.). Sage Publications.
Criado, J. I., & de Zarate-Alcarazo, L. O. (2022). Technological frames, CIOs, and artificial intelligence in public administration: A socio-cognitive exploratory study in Spanish local governments. Government Information Quarterly, 39(4), 101688. https://doi.org/10.1016/j.giq.2022.101688
Criado, J. I., Sandoval-Almazán, R., & Gil-Garcia, J. R. (2025). Artificial intelligence and public administration: Understanding actors, governance, and policy from micro, meso, and macro perspectives. Public Administration Review, 85(2), 241-262. https://doi.org/10.1111/puar.13672
Dignum, V. (2019). Responsible artificial intelligence: How to develop and use AI in a responsible way. Springer. https://doi.org/10.1007/978-3-030-30371-6
Djeffal, C., Siewert, M. B., & Wurster, S. (2022). Role of the state and responsibility in governing artificial intelligence: A comparative analysis of AI strategies. Journal of European Public Policy, 29(11), 1799-1821. https://doi.org/10.1080/13501763.2021.1956774
Eom, S. J., & Lee, J. (2022). Digital government transformation in turbulent times: Responses, challenges, and future direction. Government Information Quarterly, 39(2), 101646. https://doi.org/10.1016/j.giq.2021.101646
Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press.
European Commission. (2019). Ethics guidelines for trustworthy AI. High-Level Expert Group on Artificial Intelligence. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai
European Commission. (2021). Proposal for a regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence. COM(2021) 206 final.
Floridi, L. (2021). Translating urgency into impact: AI ethics from principles to practices. Philosophy & Technology, 34(4), 1027-1048. https://doi.org/10.1007/s13347-021-00474-3
Floridi, L. (2024). The ethics of artificial intelligence: Principles, challenges, and opportunities. Oxford Review of Economic Policy, 40(2), 282-301. https://doi.org/10.1093/oxrep/graa004
Gesk, T. S., & Leyer, M. (2022). Artificial intelligence in public services: When and why citizens accept its usage. Government Information Quarterly, 39(3), 101704. https://doi.org/10.1016/j.giq.2022.101704
Grand View Research. (2024). AI in government and public services market report, 2033. San Francisco: Grand View Research.
Grimmelikhuijsen, S. (2022). The importance of effectiveness versus transparency and stakeholder involvement in citizens' perception of public sector algorithms. Public Management Review, 25(8), 1584-1604. https://doi.org/10.1080/14719037.2022.2144938
Hainmueller, J., Hopkins, D. J., & Yamamoto, T. (2014). Causal inference in conjoint analysis: Understanding multidimensional choices via stated preference experiments. Political Analysis, 22(1), 1-30. https://doi.org/10.1093/pan/mpt024
Janssen, M., & Kuk, G. (2016). The challenges and limits of big data algorithms in technocratic governance. Government Information Quarterly, 33(3), 371-377. https://doi.org/10.1016/j.giq.2016.08.011
Kemper, J., & Kolkman, D. (2019). Transparent to whom? No algorithmic accountability without a critical audience. Information, Communication & Society, 22(14), 2081-2096. https://doi.org/10.1080/1369118X.2018.1477967
König, P. D., & Wenzelburger, G. (2020). Opportunity for renewal or disruptive force? How artificial intelligence alters democratic politics. Government Information Quarterly, 37(3), 101489. https://doi.org/10.1016/j.giq.2020.101489
Kostka, G. (2019). China's social credit systems and public opinion: Explaining high levels of approval. New Media & Society, 21(7), 1565-1593. https://doi.org/10.1177/1461444819826402
Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017). Accountable algorithms. University of Pennsylvania Law Review, 165(3), 633-705.
Larsson, S. (2020). On the governance of artificial intelligence through ethics guidelines. Asian Journal of Law and Society, 7(3), 437-451. https://doi.org/10.1017/als.2020.19
McKinsey & Company. (2024). The state of AI in 2024. McKinsey Global Institute. Retrieved from https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
McKinsey & Company. (2025). The state of AI: How organizations are rewiring to capture value. McKinsey Global Institute. Retrieved from https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
MIT Technology Review. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Cambridge: MIT Press.
National University. (2025). 131 AI statistics and trends for 2025. Retrieved from https://www.nu.edu/blog/ai-statistics-trends/
Neumann, O., Guirguis, K., & Steiner, R. (2024). Exploring artificial intelligence adoption in public organizations: A comparative case study. Public Management Review, 26(1), 114-141. https://doi.org/10.1080/14719037.2022.2048685
O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishers.
Oxford Insights. (2024). Government AI readiness index 2024. Retrieved from https://oxfordinsights.com/ai-readiness/ai-readiness-index/
Partnership for Public Service. (2024). The state of public trust in government 2024. Washington, DC: Partnership for Public Service.
Pew Research Center. (2024). Public trust in government: 1958-2024. Washington, DC: Pew Research Center.
Starke, C., Baleis, J., Keller, B., & Marcinkowski, F. (2022). Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature. Big Data & Society, 9(2), 1-21. https://doi.org/10.1177/20539517221115189
Stanford University. (2024). AI index report 2024: Large language models and dialect prejudice. Stanford HAI.
Stanford University. (2025). AI index report 2025: Annual report on artificial intelligence. Stanford HAI.
TechPolicy.Press. (2025). AI accountability starts with government transparency. Retrieved from https://www.techpolicy.press/ai-accountability-starts-with-government-transparency/
Tech Nation. (2024). UK AI sector spotlight 2024. London: Tech Nation.
Van Noordt, C., & Misuraca, G. (2022). Artificial intelligence for the public sector: Results of landscaping the use of AI in government across the European Union. Government Information Quarterly, 39(3), 101714. https://doi.org/10.1016/j.giq.2022.101714
Veale, M., & Binns, R. (2017). Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data. Big Data & Society, 4(2), 1-17. https://doi.org/10.1177/2053951717743530
Wang, Y. F., Chen, Y. C., Chien, S. Y., & Wang, P. J. (2024). Citizens' trust in AI-enabled government systems. Information Polity, 29(2), 187-203. https://doi.org/10.3233/IP-230065
Winfield, A. F., & Jirotka, M. (2018). Ethical governance is essential to building trust in robotics and artificial intelligence systems. Philosophical Transactions of the Royal Society A, 376(2133), 20180085. https://doi.org/10.1098/rsta.2018.0085
Zuiderwijk, A., Chen, Y. C., & Salem, F. (2021). Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda. Government Information Quarterly, 38(3), 101577. https://doi.org/10.1016/j.giq.2021.101577