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An Artificial Intelligence Definition and Classification Framework for Public Sector Applications
Open accessAbstract
Artificial Intelligence (AI) currently holds the spotlight amongst the interdisciplinary research fields that transcend the realm of academia. AI methods are enabled by a wide variety of technologies, methods and data, presenting a number of opportunities and challenges in a public decision-making context that differ by context of application. While the regulatory landscape surrounding AI governance is a pressing problem for scholars, the literature has often overlooked the role of the government as a user of AI. This article presents a classification framework for AI applications that facilitates the work in public sector (in the role of user) deployment. With our framework, we try to provide a lens to different deployment-related questions.
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“The experience acquired through the use of deterministic systems facilitates clarity on the public value outcomes desired from AI”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Ballester, 2021
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“In the context of AI adoption, absorptive capacity is manifested through a strong path dependency on existing infrastructure developed through previous e-government innovations, collaborations between organisations, and a network of external technical specialists”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Ballester, 2021
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“The related theme of IT capabilities identifies current capabilities in managing IT assets, basic employee knowledge in AI and big data, and a data-oriented culture essential to building AI capabilities (Ballester, 2021;Campion et al., 2020;Casalino, Saso, Borin, Massella, & Lancioni, 2020;Chatfield & Reddick, 2018;Chen, Ran, & Gao, 2019;Clarke & Margetts, 2014;Desouza et al., 2020;Giest, 2017;J”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Ballester, 2021
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“Higher discretionary tasks that may directly impact an individual or community are typically characterised by fuzzy success criteria and multiple interdependent systems that are difficult to model”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Ballester, 2021
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Cited by
Madan, R., & Ashok, M. (2023) · Government Information Quarterly · doi:10.1016/j.giq.2022.101774 · Cited by 468
Metadata
- Authors
- Ballester, O.
- Affiliations
- University of Lausanne (CH)
- Corresponding author
- Ballester, Omar
- ORCID
- Ballester, Omar
- Venue
- DG.O2021: The 22nd Annual International Conference on Digital Government Research
- Publisher
- ACM
- Published
- 2021-06-09
- Pages
- 67-75
- ISSN
- 3050-8681 (ISSN-L)
- OpenAlex
- W3170104400
- Cited by
- 28
- References
- 7
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 8 · 2023: 4 · 2022: 5
- FWCI
- 3.66
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Annual International Conference on Digital Government Research (publishedVersion)
- IRIS (submittedVersion)
- SERVAL (Université de Lausanne) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Political Science and International Relations › E-Government and Public Services
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence, public sector, AI deployment, public decision-making, AI governance, government use of AI
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