Artificial Intelligence for data-driven decision-making and governance in public affairs
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
There is little empirical research on how public procurement can be successfully realised as an AI governance mechanism in practice.
“There is little empirical research on how this could be successfully realised in practice [30].”
From “It’s Just a Wild, Wild West”: Harnessing Public Procurement as an AI Governance Mechanism (2026) · cites this paper as [30]
Cites
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., et al. (2018) · Minds and Machines · doi:10.1007/s11023-018-9482-5 · Cited by 4,774 · see citing works
What can machine learning do? Workforce implications
Brynjolfsson, E., & Mitchell, T. (2017) · Science · doi:10.1126/science.aap8062 · Cited by 1,131 · see citing works
Sun, T. Q., & Medaglia, R. (2019) · Government Information Quarterly · doi:10.1016/j.giq.2018.09.008 · Cited by 948 · see citing works
Data governance: Organizing data for trustworthy Artificial Intelligence
Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Janowski, T. (2020) · Government Information Quarterly · doi:10.1016/j.giq.2020.101493 · Cited by 811 · see citing works
A Layered Model for AI Governance
Gasser, U., & Almeida, V. A. F. (2017) · IEEE Internet Computing · doi:10.1109/mic.2017.4180835 · Cited by 431 · see citing works
An attention-based view of AI assimilation in public sector organizations: The case of Saudi Arabia
Alshahrani, A., Dennehy, D., & Mäntymäki, M. (2022) · Government Information Quarterly · doi:10.1016/j.giq.2021.101617 · Cited by 106 · see citing works
Casares, A. P. (2018) · Futures · doi:10.1016/j.futures.2018.05.002 · Cited by 54 · see citing works
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“It’s Just a Wild, Wild West”: Harnessing Public Procurement as an AI Governance Mechanism
Hudig, A. I., Kallina, E., & Singh, J. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791968 · Cited by 3 · see citing works
- Authors
- Charles, V., Rana, N. P., & Carter, L.
- Affiliations
- University of Bradford (GB)
- Qatar University (QA)
- UNSW Sydney (AU)
- Corresponding author
- Rana, Nripendra P.
- Venue
- Government Information Quarterly
- Publisher
- Elsevier BV
- Published
- 2022-10
- Volume
- 39
- Issue
- 4
- Pages
- 101742
- ISSN
- 0740-624X (ISSN-L)
- OpenAlex
- W4286449686
- Cited by
- 101
- References
- 7
- Citations by year
- 2026: 33 · 2025: 39 · 2024: 17 · 2023: 9 · 2022: 3
- FWCI
- 55.51
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Government Information Quarterly (publishedVersion)
- Topics
- Social Sciences › Political Science and International Relations › E-Government and Public Services
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Engineering › Media Technology › Smart Cities and Technologies
- Keywords
- artificial intelligence in public administration, data-driven decision making, public governance, AI governance, AI risks, citizen privacy, citizen satisfaction, public value creation, AI ethics, AI implementation challenges
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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