National strategic artificial intelligence plans: A multi-dimensional analysis
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AI practices and digital transformation strategies from the private sector cannot directly be copied to the public sector because of the public sector's need to maximize public value.
“AI practices and digital transformation strategies from the private sector cannot directly be copied to the public sector because of the public sector's need to maximize public value (Fatima, Desouza, & Dawson, 2020).”
From Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda (2021) · cites this paper as Fatima, Desouza, & Dawson, 2020
AI use in government faces ethical and legitimacy challenges, including moral dilemmas, unethical data use, AI discrimination, privacy issues, security, trust, and unfairness in public service delivery.
“Fifth, ethical and legitimacy challenges concern challenges related to moral dilemmas (Wirtz et al., 2019), unethical use of data (Fatima et al., 2020;Gupta, 2019), AI discrimination (Gomes de Sousa et al., 2019;Wirtz et al., 2019), and unethical use of shared data (the latter in the context of AI in healthcare) (Sun & Medaglia, 2019).”
From Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda (2021) · cites this paper as Fatima et al., 2020
Most selected studies addressed public governance implications of AI use at a global level, while some were scoped to national or local government levels.
“Our analysis shows that most studies addressed this topic at a global level, while some studies are scoped towards the national government level (e.g., Fatima et al., 2020;Gupta, 2019;Liu et al., 2019) or towards the local government level (e. g., Aoki, 2020;Ben Rjab & Mellouli, 2018;Ben Rjab & Mellouli, 2019).”
From Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda (2021) · cites this paper as Fatima et al., 2020
It is challenging to assign responsibilities and address accountability issues in the use of AI in the public sector.
“Hence, it is challenging to assign responsibilities and address accountability issues in the use of AI in the public sector (Fatima et al., 2020).”
From Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda (2021) · cites this paper as Fatima et al., 2020
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References 71 · Cited by 216
- Authors
- Fatima, S., Desouza, K. C., & Dawson, G. S.
- Affiliations
- Queensland University of Technology (AU)
- Arizona State University (US)
- Corresponding author
- Fatima, Samar
- Venue
- Economic Analysis and Policy
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 67
- Pages
- 178-194
- ISSN
- 0313-5926 (ISSN-L)
- OpenAlex
- W3044666894
- Cited by
- 216
- References
- 71
- Citations by year
- 2026: 40 · 2025: 54 · 2024: 54 · 2023: 31 · 2022: 25 · 2021: 11 · 2020: 1
- FWCI
- 23.14
- Type
- journal article
- Language
- en
- Open access
- green
- eprints.qut.edu.au
- Licence
- Locations
- Economic Analysis and Policy (publishedVersion)
- QUT ePrints (Queensland University of Technology) (acceptedVersion, cc-by-nc-nd)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- national AI strategy, AI policy, public administration modernization, industrial competitiveness, responsible data management, algorithmic governance, AI governance, capacity building, AI investment, technological innovation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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