How Machine Learning is Changing e-Government
Abstract
Big Data is, clearly, an integral part of modern information societies. A vast amount of data is daily produced and it is estimated that, for the years to come, this number will grow dramatically. In an effort to transform the hidden information in this ocean of data into a useful one, the use of advanced technologies, such as Machine Learning, is deemed appropriate. Machine Learning is a technology that can handle Big Data classification for statistical or even more complex purposes, such as decision making. This fits perfectly with the scope of the new generation of government, Government 3.0, which explores all the new opportunities to tackle any challenge faced by contemporary societies, by utilizing new technologies for data-driven decision making. Boosted by the opportunities, Machine Learning can facilitate more and more governments participate in the development of such applications in different governmental domains. But is the Machine Learning only beneficial for public sectors? Although there is a huge number of researches in the literature related to Machine Learning applications, there is lack of a comprehensive study focusing on the usage of this technology within governmental applications. The current paper moves towards this research question, by conducting a comprehensive analysis of the use of Machine Learning by governments. Through the analysis, quite interesting findings have been identified, containing both benefits and barriers from the public sectors' p…
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The majority of ML projects in governments are currently pilot applications.
“The majority of ML projects in governments are currently pilot applications”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Alexopoulos et al., 2019
Collaboration and sharing of data between government departments help develop better models.
“Collaboration and sharing of data between government departments (Alexopoulos et al., 2019;Janssen, Brous, et al., 2020) help develop better models.”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Alexopoulos et al., 2019
The data driving AI technologies in public administration, in particular machine learning, is Big, Open, and Linked Data (BOLD) consisting of structured and unstructured formats, generated in realtime, and dependent on multiple organisations or systems with different data management practices.
“The data driving AI technologies in public administration, in particular machine learning, is Big, Open, and Linked Data (BOLD) consisting of structured and unstructured formats, generated in realtime, and dependent on multiple organisations or systems with different data management practices”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Alexopoulos et al., 2019
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- Authors
- Alexopoulos, C., Lachana, Z., Androutsopoulou, A., Diamantopoulou, V., Charalabidis, Y., & Loutsaris, M. A.
- Affiliations
- University of the Aegean (GR)
- ORCID
- Venue
- Proceedings of the 12th International Conference on Theory and Practice of Electronic Governance
- Publisher
- ACM
- Published
- 2019-04-03
- Pages
- 354-363
- OpenAlex
- W2946333210
- Cited by
- 88
- References
- 65
- Citations by year
- 2026: 3 · 2025: 13 · 2024: 18 · 2023: 19 · 2022: 19 · 2021: 11 · 2020: 4 · 2019: 1
- FWCI
- 60.26
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Theory and Practice of Electronic Governance (publishedVersion)
- Funders
- Erasmus+
- 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
- machine learning, e-government, Government 3.0, big data, data-driven decision making, public sector, technology adoption, barriers to adoption
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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