Municipal Surveillance Regulation and Algorithmic Accountability
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Machine-learning algorithms are being introduced to support not just routine but also knowledge-intensive government tasks.
“In recent years, we have seen that these machine-learning algorithms are being introduced to support not just routine but also knowledge-intensive government tasks (Young et al. 2019;Zouridis et al. 2020).”
From Legitimacy of Algorithmic Decision-Making: Six Threats and the Need for a Calibrated Institutional Response (2022) · cites this paper as Young et al. 2019
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Agostino, D., Saliterer, I., & Steccolini, I. (2021) · Financial Accountability and Management · doi:10.1111/faam.12301 · Cited by 271 · see citing works
Grimmelikhuijsen, S., & Meijer, A. (2022) · Perspectives on Public Management and Governance · doi:10.1093/ppmgov/gvac008 · Cited by 189 · see citing works
From Banal Surveillance to Function Creep: Automated License Plate Recognition (ALPR) in Denmark
Pereira, G., & Raetzsch, C. (2022) · Surveillance & Society · doi:10.24908/ss.v20i3.15000 · Cited by 19 · see citing works
Johnson, N., Silva, E., Leon, H., Eslami, M., Schwanke, B., Dotan, R., & Heidari, H. (2025) · Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3715275.3732049 · Cited by 12 · see citing works
Harris, H., & Burke, A. (2021) · IEEE International Enterprise Distributed Object Computing Conference Workshops (EDOCW) · doi:10.1109/edocw52865.2021.00032 · Cited by 5 · see citing works
- Authors
- Young, M., Katell, M., & Krafft, P. M.
- Affiliations
- University of Washington (US)
- The Alan Turing Institute (GB)
- University of Oxford (GB)
- Corresponding author
- Young, Margaret
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2019
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W3122779198
- Cited by
- 5
- References
- 0
- Citations by year
- 2025: 1 · 2022: 2 · 2021: 2
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
- Keywords
- algorithmic accountability, algorithmic discrimination, surveillance technology, indirect discrimination, citizen participation, risk assessment tools, algorithmic regulation
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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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