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Cited by 153Ethics and Social Impacts of AIPolicing Practices and PerceptionsArtificial Intelligence in Law

The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index; none of the 30 sources its claims cite is checked for support yet.

‘Just like I thought’: Street‐level bureaucrats trust AI recommendations if they confirm their professional judgment

Open access
Selten, Friso, Robeer, Marcel, Grimmelikhuijsen, Stephan·2023·Public Administration Review
DOI 10.1111/puar.13602
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Abstract

Artificial Intelligence is increasingly used to support and improve street‐level decision‐making, but empirical evidence on how street‐level bureaucrats' work is affected by AI technologies is scarce. We investigate how AI recommendations affect street‐level bureaucrats' decision‐making and if explainable AI increases trust in such recommendations. We experimentally tested a realistic mock predictive policing system in a sample of Dutch police officers using a 2 × 2 factorial design. We found that police officers trust and follow AI recommendations that are congruent with their intuitive professional judgment. We found no effect of explanations on trust in AI recommendations. We conclude that police officers do not blindly trust AI technologies, but follow AI recommendations that confirm what they already thought. This highlights the potential of street‐level discretion in correcting faulty AI recommendations on the one hand, but, on the other hand, poses serious limits to the hope that fair AI systems can correct human biases.

Overview

CoChat Summary

TL;DR

In a 2×2 vignette experiment with 124 Dutch police officers and 294 observations, officers trusted AI recommendations more when they were congruent with their professional judgment, while explanations did not significantly affect trust. Higher trust was associated with greater likelihood of following the AI recommendation, suggesting confirmation bias rather than automation bias.

Key findings

  • H1 supported: police officers perceived AI recommendations congruent with their professional knowledge as more trustworthy than incongruent recommendations (β = −.377 [−.622, −.132], p = .003; group means on 1–7 trust scale: incongruent unexplained 4.73, incongruent explained 4.98, congruent unexplained 5.26, congruent explained 5.25).
  • H2 not supported: explained versus unexplained AI recommendations had no statistically significant effect on trust (β = −.133 [−.400, .134], p = .328); the interaction between congruence and explanation was also not significant (β = −.259 [−.888, .371], p = .42).
  • H3 supported: higher perceived trustworthiness was associated with increased likelihood of following the AI recommendation (β = .243 [.006, .480], p = .045).
  • Manipulation checks: congruency check mean 4.65, SD 1.63, t = 5.65, p < .001; explanation-detail check mean 4.49, SD 1.48, t = 3.34, p < .001; mundane realism mean 5.24, SD 1.25.

Methods

  • Design: 2×2 repeated measures factorial vignette experiment with three scenarios (burglary, ATM robbery, stabbing incident); factors were AI recommendation congruent/incongruent with intuitive professional knowledge and explained/unexplained; the mock predictive policing system was based on a system developed by the Dutch police.
  • Sample/data: population-based sample of Dutch street-level police officers; survey sent to approximately 400 officers, 152 responded, 28 excluded, final N = 124 officers and 294 vignettes; average age approximately 47.9, 25% female, 93% executive status; data collected May–June 2021.
  • Analysis: Generalized Estimating Equations (GEE) using the Geepack package in R to account for repeated measures; trust scale included four items plus a direct trust item (Cronbach’s alpha above .92; factor loadings above .74); follow/override was a dummy variable; robustness checks with two-way ANOVA and logistic regression produced equal results.

Limitations

  • The paper states that survey experiments remain artificial despite high mundane realism, and calls for field experiments in “messy” settings where police officers work in teams or under time pressure.
  • The paper states that the study focuses on police officers and one type of local XAI; it calls for testing other street-level bureaucrats, other AI systems and organizational contexts, global explanations, and repeated use of reliable AI systems.
  • Evident/paper-stated: no attention checks were used, and inclusion required completing at least one scenario, so inattentive responses may remain (the paper frames this as an intent-to-treat effect and external-validity strength); also, there was a small but statistically significant baseline difference in general trust in technology between the congruent unexplained and incongruent unexplained groups (less than 0.6 point on a 1–7 scale), which the authors do not believe substantially affected the findings.

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Integrity signals

Registry

Crossref · 10.1111/puar.13602

Cites

59 of 59 references verified0 citation errors · 0 not checkable

Statistics

4 tests recomputed · 0 with the reported and recomputed p on opposite sides of .05

Figures

Image 1
CC-BY · From the PDF
F I G U R E 2 The map used in the ATM robbery scenario
F I G U R E 2 The map used in the ATM robbery scenarioCC-BY · From the PDF
F I G U R E 3 The effect of professional judgment and explanations on police officers ' perceived trustworthiness of AI recommendations
F I G U R E 3 The effect of professional judgment and explanations on police officers ' perceived trustworthiness of AI recommendationsCC-BY · From the PDF
F I G U R E 4 The relation between police officers ' perceived trustworthiness of AI recommendations and their tendency to follow the recommendation
F I G U R E 4 The relation between police officers ' perceived trustworthiness of AI recommendations and their tendency to follow the recommendationCC-BY · From the PDF
Image 5
CC-BY · From the PDF
Image 6
CC-BY · From the PDF
Image 7
CC-BY · From the PDF

Verification

Claims the paper makes

0 Valid · 0 Not valid · 23 Not checked · 62 Own results and interpretations

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  • Probably not a substantial claim. 41
  • It may state two separate claims as one. 19
  • The quote only partly states this claim. 1

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Statistics that don’t add up

Each reported p-value recomputed from its test statistic and degrees of freedom.

1 of 1

Statistics recomputed

Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.

  • Doesn’t add upF(3, 280) = 0.182reported p = .14 · recomputed p = .909

    “F(3, 280) = 0.182, p = .14”

  • ConsistentF(3, 280) = 0.81reported p = .49 · recomputed p = .489

    “F(3, 280) = 0.81, p = .49”

  • ConsistentF(3, 280) = 0.53reported p = .66 · recomputed p = .662

    “F(3, 280) = 0.53, p = .66”

  • ConsistentF(3, 280) = 2.89reported p = .04 · recomputed p = .036

    “F(3, 280) = 2.89, p = .04”

4 of 4

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

1 of 1

Citations

Cites

Research works printed but not linked: 2

  1. Kahneman Daniel, 2013, Thinking, Fast and Slow
  2. O'Neill Cathy, 2016, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

Cited by

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Related

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Metadata

Authors
Selten, F., Robeer, M., & Grimmelikhuijsen, S.
Affiliations
  • Leiden University (NL)
  • Utrecht University (NL)
Corresponding author
Selten, Friso
ORCID
Venue
Public Administration Review
Publisher
Wiley
Published
2023-01-22
Volume
83
Issue
2
Pages
263-278
ISSN
  • 0033-3352 (ISSN-L)
  • 1540-6210
DOI
10.1111/puar.13602
OpenAlex
W4313590506
Cited by
153
References
58
Citations by year
2026: 73 · 2025: 52 · 2024: 19 · 2023: 8
FWCI
10.53
Type
journal article
Language
en
Open access
Licence
Locations
  • Public Administration Review (publishedVersion, cc-by)
  • Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
Funders
Nederlandse Organisatie voor Wetenschappelijk Onderzoek (406.DI.19.011)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Social Sciences › Political Science and International Relations › Policing Practices and Perceptions
  • Social Sciences › Law › Artificial Intelligence in Law
Keywords
street-level bureaucracy, artificial intelligence, trust in AI, explainable AI, police decision-making, predictive policing, professional judgment, algorithmic decision support, human-AI interaction, algorithmic fairness, human bias

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Statistics and arithmetic are recomputed, not judged.

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Data sources

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.