Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/3657054.3657249acmotherconferencesArticle/Chapter ViewAbstractPublication Pagesdg-oConference Proceedingsconference-collections
research-article

Impact and barriers to AI in the public sector: the case of the State of Mexico

Published: 11 June 2024 Publication History

Abstract

The use and implementation of Artificial Intelligence (AI) tools for doing repetitive tasks in the public sector is a challenge, particularly in persuading bureaucrats. However, the potential benefits for citizens, such as improved process and services related to tax payments and basic services using machine learning or diffuse logic for decision making or logistic distribution, are significant. This research aims to understand the perceptions of public managers regarding the impact, functions, and barriers of AI in the context of a local government. A survey was conducted among 32 key public managers from the government of the State of Mexico in the central region to assess their perceptions of AI. The findings indicate that there is widespread concern among public administrators regarding high costs, suggesting the critical need to address financial issues to ensure sustainable implementation of AI. In terms of barriers, the results underscore the urgent necessity of addressing fundamental issues such as connectivity, financial resources, and technological capacity to enable effective integration of AI. This study is relevant as it identifies the key aspects of impact, functions, and barriers for the implementation of AI in a local government.

References

[1]
Chen, T. 2023. The Adoption and Implementation of Artificial Intelligence Chatbots in Public Organizations: Evidence from U.S. State Governments. The American Review of Public Administration. (Sep. 2023).
[2]
Criado, J.I. 2020. Chief information officers’ perceptions about artificial intelligence. First Monday. (Dec. 2020).
[3]
Filgueiras, F. Inteligencia Artificial en la administración pública: ambigüedad y elección de sistemas de IA y desafíos de gobernanza digital.
[4]
Hassan, M.S. 2023. Generative Artificial Intelligence (ChatGPT & Bard) in Public Administration Research: A Double-Edged Sword for Street-Level Bureaucracy Studies. International Journal of Public Administration. (Nov. 2023).
[5]
Marzouki, A. 2023. Barriers and actions for the adoption and use of Artificial Intelligence in the public sector. Proceedings of the 16th International Conference on Theory and Practice of Electronic Governance (Belo Horizonte Brazil, Sep. 2023), 94–100.
[6]
Millan Vargas, A. and Sandoval-Almazán, R. 2024. Public managers perception on artificial intelligence: the case of the State of Mexico.
[7]
Nadim, B. 2023. Applying Artificial Intelligence in e-governments Challenges and Barriers - Thematic Review. (Jun. 2023).
[8]
Nurski, L. 2023. Artificial intelligence adoption in the public sector: A case study. Technical Report #03/2023. Bruegel.
[9]
Russell, S.J. 2010. Artificial intelligence: a modern approach. Prentice Hall.
[10]
Ruvalcaba Gómez, E. and Cifuentes Faura, J. 2023. Analysis of the perception of digital government and artificial intelligence in the public sector in Jalisco, Mexico. International Review of Administrative Sciences. 89, (Mar. 2023), 002085232311645.
[11]
Valle-Cruz, D. and García-Contreras, R. 2023. Towards AI-driven transformation and smart data management: Emerging technological change in the public sector value chain. Public Policy and Administration. (Jul. 2023), 09520767231188401. >
[12]
Wirtz, B. 2018. Artificial Intelligence and the Public Sector—Applications and Challenges. International Journal of Public Administration. 42, (Jul. 2018).
[13]
Valle-Cruz, D., Fernandez-Cortez, V., & Gil-Garcia, J. R. (2022). From E-budgeting to smart budgeting: Exploring the potential of artificial intelligence in government decision-making for resource allocation. Government Information Quarterly, 39(2), 101644.
[14]
Bullock, J. B. (2019). Artificial intelligence, discretion, and bureaucracy. The American Review of Public Administration, 49(7), 751-761.
[15]
Matheus, R., Janssen, M., & Maheshwari, D. (2020). Data science empowering the public: Data-driven dashboards for transparent and accountable decision-making in smart cities. Government Information Quarterly, 37(3), 101284.
[16]
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.
[17]
Valle-Cruz, D., García-Contreras, R., & Gil-Garcia, J. R. (2023). Exploring the negative impacts of artificial intelligence in government: the dark side of intelligent algorithms and cognitive machines. International Review of Administrative Sciences, 00208523231187051.

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences
dg.o '24: Proceedings of the 25th Annual International Conference on Digital Government Research
June 2024
1089 pages
ISBN:9798400709883
DOI:10.1145/3657054
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 June 2024

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. artificial intelligence
  2. barriers
  3. digital government
  4. functions
  5. perceptions
  6. public managers
  7. public sector

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

dg.o 2024

Acceptance Rates

Overall Acceptance Rate 150 of 271 submissions, 55%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 54
    Total Downloads
  • Downloads (Last 12 months)54
  • Downloads (Last 6 weeks)16
Reflects downloads up to 19 Nov 2024

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media