AI-driven public consultation platforms
Transforming civic engagement in local governments
DOI:
https://doi.org/10.59490/dgo.2025.953Keywords:
Artificial Intelligence, Local governments, Civic engagement, Technology adoptionAbstract
The integration of artificial intelligence (AI) in local governments has gained increasing attention as a means to enhance civic engagement and streamline public participation. This study examines how local governments utilize AI-powered platforms for civic engagement, exploring the perspectives of public servants and developers on the implementation of AI, its benefits, challenges, and ethical considerations. With a qualitative approach, semi-structured interviews were conducted with developers, product designers, public servants, and decision-makers involved in developing and using the AI-powered platform for civic engagement initiatives. Thematic analysis was employed to identify key themes related to AI functionalities, user experiences, and governance challenges. The preliminary results reveal that four distinct types of AI applications have been employed, including AI-powered optical character recognition (OCR), AI-based content moderation, AI sensemaking, and AI-driven translation. These AI tools facilitate efficiency in data processing, enhance accessibility, and support decision-making. However, concerns regarding AI transparency, data privacy, and public trust remain significantly challenging. Additionally, public servants emphasized the need for AI literacy training and the development of ethical guidelines to ensure the responsible use of AI in local governance. This study contributes to the growing discourse on AI in civic engagement by offering insights into the practical and ethical dimensions of AI adoption in local governments. The findings underscore the need for policy frameworks, digital inclusivity measures, and ongoing capacity-building efforts to enhance AI-driven public participation.
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Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021). Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. Journal of Cleaner Production, 289, 125834. https://doi.org/10.1016/j.jclepro.2021.125834
Ahn, M. J., & Chen, Y. C. (2020). Artificial intelligence in government: Potentials, challenges, and the future. The 21st Annual International Conference on Digital Government Research, 243–252. https://doi.org/10.1145/3396956.3398260
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, 39(2), 101664. https://doi.org/10.1016/j.giq.2021.101664
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
Androutsopoulou, A., Karacapilidis, N., Loukis, E., & Charalabidis, Y. (2019). Transforming the communication between citizens and government through AI-guided chatbots. Government Information Quarterly, 36(2), 358–367. https://doi.org/10.1016/j.giq.2018.10.001
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
Axelsson, K., Melin, U., & Lindgren, I. (2010). Exploring the importance of citizen participation and involvement in e-government projects: Practice, incentives, and organization. Transforming Government: People, Process and Policy, 4(4), 299–321. https://doi.org/10.1108/17506161011081309
Bonsón, E., Royo, S., & Ratkai, M. (2015). Citizens’ engagement on local governments’ Facebook sites. An empirical analysis: The impact of different media and content types in Western Europe. Government Information Quarterly, 32(1), 52–62. https://doi.org/10.1016/j.giq.2014.11.001
Charles, V., Rana, N. P., & Carter, L. (2022). Artificial Intelligence for data-driven decision-making and governance in public affairs. Government Information Quarterly, 39(4), 101742. https://doi.org/10.1016/j.giq.2022.101742
Dhieb, N., Ghazzai, H., Besbes, H., & Massoud, Y. (2020). A secure ai-driven architecture for automated insurance systems: Fraud detection and risk measurement. IEEE Access, 8, 58546-58558. https://doi.org/10.1109/ACCESS.2020.2983300
Dumas, C. L., LaManna, D., Harrison, T. M., Ravi, S., Kotfila, C., Gervais, N., Hagen, L., & Chen, F. (2015). Examining political mobilization of online communities through e-petitioning behavior in We the People. Big Data & Society, 2(2), 2053951715598170. https://doi.org/10.1177/2053951715598170
Glaser, B., & Strauss, A. (1967). The discovery of grounded theory: Strategies for qualitative research. Aldine Transaction.
Gualdi, F., & Cordella, A. (2021). Artificial Intelligence and decision-making: The question of accountability. In Proceedings of the 54th Hawaii International Conference on System Sciences, 2297–2306. https://doi.org/10.24251/HICSS.2021.281
Hagen, L. (2018). Content analysis of e-petitions with topic modeling: How to train and evaluate LDA models? Information Processing & Management, 54(6), 1292–1307. https://doi.org/10.1016/j.ipm.2018.05.006
Henk, A., & Nilssen, F. (2021). Can AI become a state servant? A case study of an intelligent chatbot implementation in a Scandinavian public service. In Proceedings of the 54th Hawaii International Conference on System Sciences (HICSS). [link]
Hood, C., & Margetts, H. (2007). The Tools of Government in the Digital Age. Bloomsbury Publishing.
Kuziemski, M., & Misuraca, G. (2020). AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings. Telecommunications Policy, 44(6), 101976. https://doi.org/10.1016/j.telpol.2020.101976
Loi, M., & Spielkamp, M. (2021). Towards accountability in the use of Artificial Intelligence for public administrations. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 757–766. https://doi.org/10.1145/3461702.3462631
Long, D., & Magerko, B. (2020). What is AI Literacy? Competencies and Design Considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. https://doi.org/10.1145/3313831.3376727
Maragno, G., Tangi, L., Gastaldi, L., & Benedetti, M. (2021). The spread of Artificial Intelligence in the public sector: A worldwide overview. 14th International Conference on Theory and Practice of Electronic Governance, 1–9. https://doi.org/10.1145/3494193.3494194
Medaglia, R., & Tangi, L. (2022). The adoption of Artificial Intelligence in the public sector in Europe: Drivers, features, and impacts. 15th International Conference on Theory and Practice of Electronic Governance, 10–18. https://doi.org/10.1145/3560107.3560110
Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020). From what to how: An initial review of publicly available AI ethics tools, methods and research to translate principles into practices. Science and Engineering Ethics, 26(4), 2141–2168. https://doi.org/10.1007/s11948-019-00165-5
OECD (2019). Recommendation of the Council on Artificial Intelligence. Retrieved from [link]
Savoldelli, A., Codagnone, C., & Misuraca, G. (2014). Understanding the e-government paradox: Learning from literature and practice on barriers to adoption. Government Information Quarterly, 31, S63–S71. https://doi.org/10.1016/j.giq.2014.01.008
Sharma, S. K., Al-Badi, A., Rana, N. P., & Al-Azizi, L. (2018). Mobile applications in government services (mG-App) from user’s perspectives: A predictive modeling approach. Government Information Quarterly, 35(4), 557–568. https://doi.org/10.1016/j.giq.2018.07.002
Sousa, W. G. D., Melo, E. R. P. D., Bermejo, P. H. D. S., Farias, R. A. S., & Gomes, A. O. (2019). How and where is artificial intelligence in the public sector going? A literature review and research agenda. Government Information Quarterly, 36(4), 101392. https://doi.org/10.1016/j.giq.2019.07.004
Tangi, L., van Noordt, C., & Rodriguez Müller, A. P. (2023). The challenges of AI implementation in the public sector. An in-depth case studies analysis. Proceedings of the 24th Annual International Conference on Digital Government Research, 414–422. https://doi.org/10.1145/3598469.3598516
Uslaner, E. M., & Brown, M. (2005). Inequality, trust, and civic Engagement. American Politics Research, 33(6), 868–894. https://doi.org/10.1177/1532673X04271903
Valle-Cruz, D., Alejandro Ruvalcaba-Gomez, E., Sandoval-Almazan, R., & Ignacio Criado, J. (2019). A review of artificial intelligence in government and its potential from a public policy perspective. Proceedings of the 20th Annual International Conference on Digital Government Research, 91–99. https://doi.org/10.1145/3325112.3325242
van Noordt, C., & Misuraca, G. (2020). Evaluating the impact of artificial intelligence technologies in public services: Towards an assessment framework. Proceedings of the 13th International Conference on Theory and Practice of Electronic Governance, 8–16. https://doi.org/10.1145/3428502.3428504
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
Wang, D., Churchill, E., Maes, P., Fan, X., Shneiderman, B., Shi, Y., & Wang, Q. (2020). From Human-Human Collaboration to Human-AI Collaboration: Designing AI Systems That Can Work Together with People. Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, 1–6. https://doi.org/10.1145/3334480.3381069
Wihlborg, E., Larsson, H., & Hedström, K. (2016). “The computer says no!” — A case study on automated decision-making in public authorities. In 2016 49th Hawaii International Conference on System Sciences (HICSS), 2903–2912. https://doi.org/10.1109/HICSS.2016.364
Wirtz, B. W., Weyerer, J. C., & Sturm, B. J. (2020). The dark sides of Artificial Intelligence: An integrated AI governance framework for public administration. International Journal of Public Administration, 43(9), 818–829. https://doi.org/10.1080/01900692.2020.1749851
Yigitcanlar, T., Agdas, D., & Degirmenci, K. (2023). Artificial intelligence in local governments: Perceptions of city managers on prospects, constraints and choices. AI & SOCIETY, 38(3), 1135–1150. https://doi.org/10.1007/s00146-022-01450-x
Zhang, W., Zuo, N., He, W., Li, S., & Yu, L. (2021). Factors influencing the use of artificial intelligence in government: Evidence from China. Technology in Society, 66, 101675. https://doi.org/10.1016/j.techsoc.2021.101675
Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X., Liu, Z., … Wen, J.-R. (2024). A Survey of Large Language Models (arXiv:2303.18223). arXiv. https://doi.org/10.48550/arXiv.2303.18223
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
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