Privacy after the Agile Turn
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Technological trust producers rely on automation, the minimization of human oversight, and the ability continuously to upgrade their technical design in response to changes in their technical and social environment.
“Technological trust producers rely on automation, the minimization of human oversight, and the ability continuously to upgrade their technical design in response to the changes in their technical and social environment (Gürses and van Hoboken, 2018).”
From Mediated trust: A theoretical framework to address the trustworthiness of technological trust mediators (2021) · cites this paper as Gürses and van Hoboken, 2018
Cited by
Sorted by citations
Madaio, M. A., Stark, L., Wortman Vaughan, J., & Wallach, H. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376445 · Cited by 436 · see citing works
Discrimination, artificial intelligence, and algorithmic decision-making
Borgesius, F. Z. (2025) · arXiv · doi:10.48550/arxiv.2510.13465 · Cited by 152 · see citing works
Security and Privacy of mHealth Applications: A Scoping Review
Nurgalieva, L., O'Callaghan, D., & Doherty, G. (2020) · IEEE Access · doi:10.1109/access.2020.2999934 · Cited by 132 · see citing works
Bodó, B. (2021) · New Media & Society · doi:10.1177/1461444820939922 · Cited by 137 · see citing works
How Society Can Maintain Human-Centric Artificial Intelligence
Bryson, J. J., & Theodorou, A. (2019) · Translational Systems Sciences · doi:10.1007/978-981-13-7725-9_16 · Cited by 94 · see citing works
Towards eXplainable Artificial Intelligence (XAI) in Tax Law: The Need for a Minimum Legal Standard
Kuźniacki, B., Almada, M., Tyliński, K., Górski, L., Winogradska, B., & Zeldenrust, R. (2022) · World Tax Journal · doi:10.59403/2yhh9pa · Cited by 31 · see citing works
Schiffner, S., Berendt, B., Siil, T., Degeling, M., Riemann, R., Schaub, F., Wuyts, K., Attoresi, M., Gürses, S., Klabunde, A., et al. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-02547-2_2 · Cited by 11 · see citing works
Self-citation
- Authors
- Gürses, S., & van Hoboken, J.
- Venue
- The Cambridge Handbook of Consumer Privacy
- Publisher
- Cambridge University Press
- Published
- 2018-04-18
- Pages
- 579-601
- OpenAlex
- W4300869287
- Cited by
- 70
- References
- 0
- Citations by year
- 2026: 6 · 2025: 2 · 2024: 4 · 2023: 7 · 2022: 5 · 2021: 9 · 2020: 27 · 2019: 5 · 2018: 4 · 2017: 1
- FWCI
- 62.97
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Cambridge University Press eBooks (publishedVersion)
- UvA-DARE (University of Amsterdam) (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- privacy, agility, agile organizations, privacy law, data protection, organizational change
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.
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