Generating Value from Government Data Using AI: An Exploratory Study
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Collaboration with private technology companies is key for the development of AI solutions in public administration which generally lack technical expertise.
“Collaboration with private technology companies is key for the development of AI solutions in public administration which generally lack technical expertise”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Gao & Janssen, 2020
For complex interdependent problems, AI-augmented decision-making uncovers new options, anomaly detection, rigorous risk identification, and better service planning and interventions.
“For complex interdependent problems, AI-augmented decision-making uncovers new options, anomaly detection, rigorous risk identification, and better service planning and interventions”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Gao & Janssen, 2020
Cites
Sun, T. Q., & Medaglia, R. (2019) · Government Information Quarterly · doi:10.1016/j.giq.2018.09.008 · Cited by 948 · see citing works
The challenges and limits of big data algorithms in technocratic governance
Janssen, M., & Kuk, G. (2016) · Government Information Quarterly · doi:10.1016/j.giq.2016.08.011 · Cited by 363 · cited 1× · see citing works
“With the use of AI algorithm, the data collection and processing become less visible [34].”
MethodSelf-citationOsman, I. H., Anouze, A. L., Irani, Z., Lee, H., Medeni, T. D., & Weerakkody, V. (2019) · European Journal of Operational Research · doi:10.1016/j.ejor.2019.02.018 · Cited by 63 · see citing works
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Madan, R., & Ashok, M. (2023) · Government Information Quarterly · doi:10.1016/j.giq.2022.101774 · Cited by 468 · see citing works
- Authors
- Gao, Y., & Janssen, M.
- Affiliations
- Huazhong University of Science and Technology (CN)
- Corresponding author
- Gao, Yingying
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Volume
- LNCS-12219
- Pages
- 319-331
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3081071836
- Cited by
- 34
- References
- 3
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 8 · 2023: 6 · 2022: 6 · 2021: 5
- FWCI
- 20.06
- Type
- conference paper
- Language
- en
- Open access
- green
- inria.hal.science
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- HAL (submittedVersion, cc-by)
- Lirias (submittedVersion)
- Research Repository (Delft University of Technology) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Topics
- Social Sciences › Political Science and International Relations › E-Government and Public Services
- Physical Sciences › Engineering › Media Technology › Smart Cities and Technologies
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- open government data, artificial intelligence, value creation, public sector data use, data privacy, algorithmic bias, Internet of Things data, transparency and accountability, crime prevention, AI policy
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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