The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
From satisficing to artificing: The evolution of administrative decision-making in the age of the algorithm
Abstract
Algorithmic decision tools (ADTs) are being introduced into public sector organizations to support more accurate and consistent decision-making. Whether they succeed turns, in large part, on how administrators use these tools. This is one of the first empirical studies to explore how ADTs are being used by Street Level Bureaucrats (SLBs). The author develops an original conceptual framework and uses in-depth interviews to explore whether SLBs are ignoring ADTs (algorithm aversion); deferring to ADTs (automation bias); or using ADTs together with their own judgment (an approach the author calls “artificing”). Interviews reveal that artificing is the most common use-type, followed by aversion, while deference is rare. Five conditions appear to influence how practitioners use ADTs: (a) understanding of the tool (b) perception of human judgment (c) seeing value in the tool (d) being offered opportunities to modify the tool (e) alignment of tool with expectations.
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“Indeed, explorations in case studies by for example Meijer et al. (2021) and Snow (2021) suggest that streetlevel bureaucrats place a great deal of trust in these professional intuitions and it is unlikely that AI recommendations will be trusted if they go against their intuitive professional knowledge.”
From ‘Just like I thought’: Street‐level bureaucrats trust AI recommendations if they confirm their professional judgment (2023) · cites this paper as Snow, 2021
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Cites
Algorithm aversion: People erroneously avoid algorithms after seeing them err.
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Automation bias: a systematic review of frequency, effect mediators, and mitigators
Goddard, K., Roudsari, A., & Wyatt, J. C. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000089 · Cited by 1,162 · cited 1×
“Automation bias manifests in both errors of commission (following incorrect algorithmic advice) and omission (failing to act because the algorithmic tool does not prompt the practitioner to do so) (Goddard et al., 2012).”
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“While there is significant research on the impact of technology on SLB discretion (Ellis, 2011; Hoybye-Mortensen, 2015; Busch and Henriksen, 2018), insufficient research considers how SLBs use ADTs to inform their decision processes (Green and Chen, 2019).”
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Cited by
Selten, F., Robeer, M., & Grimmelikhuijsen, S. (2023) · Public Administration Review · doi:10.1111/puar.13602 · Cited by 153
Bashkirova, A., & Krpan, D. (2024) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2024.100066 · Cited by 55
Rosbach, E., Ammeling, J., Krügel, S., Kießig, A., Fritz, A., Ganz, J., Puget, C., Donovan, T., Klang, A., Köller, M. C., et al. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713319 · Cited by 18
Metadata
- Authors
- Snow, T.
- ORCID
- Snow, Thea
- Venue
- Data & Policy
- Publisher
- Cambridge University Press (CUP)
- Published
- 2021
- Volume
- 3
- ISSN
- 2632-3249
- Cited by
- 31
- References
- 12
- Language
- en
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