IDEAS home Printed from https://ideas.repec.org/p/osf/socarx/vm43w.html

How to Promote AI in the US Federal Government: Insights from Policy Process Frameworks

Author

Listed:
  • Khan, Muhammad Salar
  • Shoaib, Azka
  • Arledge, Elizabeth

Abstract

When it comes to routine government activities, such as immigration, justice, social welfare provision and climate change, many people believe that the US federal government operates slowly. One potential solution to increase the productivity and efficiency of the federal government is to adopt AI technologies and devices. AI technologies and devices already provide unique capabilities, services, and products, as demonstrated by smart homes, autonomous vehicles, delivery drones, GPS navigation, and virtual assistants such as Amazon's Alexa. However, incorporating massive AI into the US federal government would present several challenges, including ethical and legal concerns, outdated infrastructure, unprepared human capital, institutional obstacles, and a lack of social acceptance. How can US policymakers promote policies that increase AI usage in the face of these challenges? This will require a comprehensive strategy at the intersection of science, policy, and economics that addresses the aforementioned challenges. In this paper, we survey literature on the interrelated policy process to understand the advancement, or lack thereof, of AI in the US federal government, an emerging area of interest. To accomplish this, we examine several policy process frameworks, including the Advocacy Coalition Framework (ACF), Multiple Streams Approach (MSA), Punctuated Equilibrium Theory, Internal Determinants and Diffusion (ID&D), Narrative Policy Framework (NPF), and Institutional Analysis and Development (IAD). We hope that insights from this literature will identify a set of policies to promote AI-operated functionalities in the US federal government.

Suggested Citation

  • Khan, Muhammad Salar & Shoaib, Azka & Arledge, Elizabeth, 2023. "How to Promote AI in the US Federal Government: Insights from Policy Process Frameworks," SocArXiv vm43w, Center for Open Science.
    • Handle: RePEc:osf:socarx:vm43w
    DOI: 10.31219/osf.io/vm43w
    as

    Download full text from publisher

    File URL: https://osf.io/download/6446babdc76c074cc1135d4f/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/vm43w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kumar, Shashank & Raut, Rakesh D. & Queiroz, Maciel M. & Narkhede, Balkrishna E., 2021. "Mapping the barriers of AI implementations in the public distribution system: The Indian experience," Technology in Society, Elsevier, vol. 67(C).
    2. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation, and Work," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 197-236, National Bureau of Economic Research, Inc.
    3. Christoph H. Loch & Bernardo A. Huberman, 1999. "A Punctuated-Equilibrium Model of Technology Diffusion," Management Science, INFORMS, vol. 45(2), pages 160-177, February.
    4. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation and Work," Boston University - Department of Economics - Working Papers Series dp-298, Boston University - Department of Economics.
    5. Jenkins-Smith, Hank C. & Sabatier, Paul A., 1994. "Evaluating the Advocacy Coalition Framework," Journal of Public Policy, Cambridge University Press, vol. 14(2), pages 175-203, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Joshua S. Gans, 2023. "Artificial intelligence adoption in a competitive market," Economica, London School of Economics and Political Science, vol. 90(358), pages 690-705, April.
    2. Kelvin K. F. Law & Michael Shen, 2025. "How Does Artificial Intelligence Shape Audit Firms?," Management Science, INFORMS, vol. 71(5), pages 3641-3666, May.
    3. Estrada, Mario Arturo Ruiz & Park, Donghyun & Staniewski, Marcin, 2023. "Artificial Intelligence (AI) can change the way of doing policy modelling," Journal of Policy Modeling, Elsevier, vol. 45(6), pages 1099-1112.
    4. Mahmut Ozer & Matjaž Perc, 2020. "Dreams and realities of school tracking and vocational education," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 6(1), pages 1-7, December.
    5. Sowa, Konrad & Przegalinska, Aleksandra & Ciechanowski, Leon, 2021. "Cobots in knowledge work," Journal of Business Research, Elsevier, vol. 125(C), pages 135-142.
    6. Chen, Xiaoxiao & Yang, Jiayi & Wu, Bo, 2025. "How is artificial intelligence shaping the labor demand of firms? ——evidence from text-mining analysis of listed companies," Telecommunications Policy, Elsevier, vol. 49(10).
    7. Huajie Jiang & Qiguo Gong, 2022. "Does Skill Polarization Affect Wage Polarization? U.S. Evidence 2009–2021," Sustainability, MDPI, vol. 14(21), pages 1-17, October.
    8. David Rezza Baqaee & Emmanuel Farhi, 2018. "Macroeconomics with Heterogeneous Agents and Input-Output Networks," NBER Working Papers 24684, National Bureau of Economic Research, Inc.
    9. Haonan Wang & Fangjuan Qiu, 2025. "AI adoption and labor cost stickiness: based on natural language and machine learning," Information Technology and Management, Springer, vol. 26(2), pages 163-184, June.
    10. Anna-Maria Kanzola, 2024. "The Knowledge Content of the Greek Production Structure in the Aftermath of the Greek Crisis," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 936-957, March.
    11. Fulvio Castellacci, 2023. "Innovation and social welfare: A new research agenda," Journal of Economic Surveys, Wiley Blackwell, vol. 37(4), pages 1156-1191, September.
    12. repec:eur:ejesjr:364 is not listed on IDEAS
    13. Xiangyi Li & Qing Wang & Ying Tang, 2024. "The Impact of Artificial Intelligence Development on Urban Energy Efficiency—Based on the Perspective of Smart City Policy," Sustainability, MDPI, vol. 16(8), pages 1-22, April.
    14. Zilian, Laura S. & Zilian, Stella S. & Jäger, Georg, 2021. "Labour market polarisation revisited: evidence from Austrian vacancy data," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 55, pages 1-007.
    15. Keeheon Lee, 2021. "A Systematic Review on Social Sustainability of Artificial Intelligence in Product Design," Sustainability, MDPI, vol. 13(5), pages 1-29, March.
    16. Sasaki, Hiroaki, 2023. "Growth with automation capital and declining population," Economics Letters, Elsevier, vol. 222(C).
    17. Elizabeth Fisher & Michael A. Flynn & Preethi Pratap & Jay A. Vietas, 2023. "Occupational Safety and Health Equity Impacts of Artificial Intelligence: A Scoping Review," IJERPH, MDPI, vol. 20(13), pages 1-28, June.
    18. Shan Feng & Shuguang Liu, 2023. "Does AI Application Matter in Promoting Carbon Productivity? Fresh Evidence from 30 Provinces in China," Sustainability, MDPI, vol. 15(23), pages 1-19, November.
    19. Venturini, Francesco, 2022. "Intelligent technologies and productivity spillovers: Evidence from the Fourth Industrial Revolution," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 220-243.
    20. Gilbert Cette & Lorraine Koehl & Thomas Philippon, 2019. "The Labor Share in the Long Term: A Decline?," Post-Print hal-02446713, HAL.
    21. Sossdorf, Fernando, 2022. "Winners take all (the most): The effects of market concentration on labor share and wage inequality," MPRA Paper 113642, University Library of Munich, Germany.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:socarx:vm43w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://arabixiv.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.