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Artificial Intelligence in Government: Why People Feel They Lose Control

T0 review · 3 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Even purely beneficial AI in routine government tasks lowers citizens' perceived control, and highlighting delegation risks—opacity, dependency, and weak contestability—reduces institutional trust and support for AI.

desk verdict A careful, pre-registered vignette study with a real finding—efficiency-framed AI raises trust but lowers perceived control—but the risk vignettes are so strongly worded that the three PAT-tension mechanism claims don't fully survive scrutiny. read the letter →

arxiv 2505.01085 v1 pith:NZN6ROT6 submitted 2025-05-02 cs.CY cs.AI

Alexander Wuttke , Adrian Rauchfleisch , Andreas Jungherr This is my paper · ORCID
classification cs.CYcs.AI
keywords principal-agenttheoryAIgovernanceexplainablehuman-AIinteractionfactorialsurveyexperimentalgorithmicfairnessinstitutionaltrustperceivedcontrol
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that when governments hand tasks to AI, they are delegating authority in the classic principal-agent sense, and that this delegation carries three built-in tensions—assessability, dependency, and contestability—that ordinary citizens sense even when the AI performs well. In a pre-registered factorial vignette experiment run in the United Kingdom (about 1,200 participants; tax, welfare, and bail domains), a purely efficiency-focused description of AI in government raised institutional trust by roughly half a point on a 7-point scale but simultaneously increased perceived loss of control by 0.36 points. When the same benefits were paired with a paragraph highlighting opacity, lock-in, or weak appeal channels, trust fell below human-only levels, perceived control loss rose by more than a point, and between two-thirds and 70 percent of respondents said the state should use less AI. The authors interpret this as a 'failure-by-success' dynamic: early functional gains legitimise AI, but awareness of the structural risks of delegation corrodes the trust and sense of democratic control that legitimacy depends on.

What carries the argument

Principal-agent theory, applied to AI as a delegated agent: the state (principal) hands tasks to an AI system (agent) under information asymmetry and incomplete control. The theory supplies three named tensions—assessability, the opacity of the AI's decision logic; dependency, the irreversibility of delegation once human expertise is eroded; and contestability, the absence of effective appeal or sanction. The empirical machinery is a pre-registered factorial vignette survey (UK, N = 1,198 after attention checks; three policy domains crossed with five conditions; three vignettes per respondent), analysed with linear mixed-effects models for trust and control and cumulative-link ordinal models for AI preference. The vignettes are the operative mechanism: every AI condition pairs the same benefit paragraph with a paragraph that makes one tension concrete.

What would settle it

Run a follow-up experiment that keeps the structural content identical but strips the evaluative language from the risk vignettes—for instance, stating neutrally that the AI's criteria are undisclosed, that appeal rates are low, or that former staff have left—and see whether trust and perceived control still move. A second check would observe citizens who actually interact with an AI-run government service and attempt to contest a decision, measuring trust and control before and after the interaction.

Watch

Extended reading notes

Core claim

The central claim is that delegating government authority to AI is a principal-agent problem, and its three structural tensions—can decisions be understood (assessability), can delegation be reversed (dependency), and can decisions be challenged (contestability)—predict citizens' trust, perceived control, and support for AI. The experiment shows that even the most favourable presentation of AI, one emphasising speed, cost savings, and error reduction, leaves citizens feeling less in control ($b = 0.36$; $p < 0.001$). When any of the three tensions is made salient, institutional trust drops by roughly 0.45–0.55 points relative to human administration, perceived loss of control rises by 1.2–1.3 points, and between 65.6% and 70.5% of respondents demand less AI use. These effects appear consistently across tax, welfare, and bail decisions. The paper concludes that awareness of delegation risks, not AI malfunction, drives the erosion of trust and control—so even successful AI implementation can undermine democratic legitimacy.

Load-bearing premise

The causal inference that the three tensions drive the attitude shifts assumes the vignettes manipulated only the intended structural feature and did not themselves supply the measured attitudes—yet the risk vignettes explicitly assert that the state has become 'dangerously dependent', that the human becomes 'a mere rubber stamp', and that the result is 'a dehumanised bureaucratic apparatus', so the drops in trust and control could reflect what the text urged rather than what citizens inferred.

Editorial extensions

If this is right

  • Any government that advertises AI's efficiency should expect an immediate legitimacy trade-off: trust rises modestly while perceived control falls.
  • Making delegation risks salient flips public demand: across all three risk conditions, between 65.6% and 70.5% of respondents said the state should use less AI, compared with 45.4% who wanted more AI in the human condition.
  • Early acceptance does not predict long-run acceptance: if awareness of AI's inner workings grows with broader use, the same public that welcomed efficiency gains will later show lower trust and control.
  • Explainability tools alone are unlikely to neutralise the problem, because the assessability condition—which mirrors the explainable-AI agenda—produced losses of trust and control as large as the dependency and contestability conditions.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The vignette wording may mean the effects partly measure message compliance rather than reasoning from structure; a valence-controlled replication that varies emotional phrasing while holding the structural facts equal would separate the two.
  • The same delegation logic should apply outside government: employers, platforms, and hospitals that hand consequential decisions to opaque, hard-to-reverse, hard-to-appeal systems should see analogous drops in trust and a sense of control among affected people.
  • The contestability result implies a concrete policy lever: if appeal channels are visible, cheap, and staffed by humans before AI is deployed, the control-loss effect may shrink; this follows from the paper's data but is not tested there.
  • Because the sample is a UK online panel stratified on demographics rather than a probability sample, the effect sizes are likely to differ across political cultures; comparative replications with representative samples would test how far the failure-by-success dynamic generalises.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. This paper applies principal-agent theory to governmental AI use, identifying three structural tensions — assessability, dependency, and contestability — and proposes a 'failure-by-success' dynamic in which efficiency gains initially raise trust while eroding perceived control, and later awareness of delegation risks undermines democratic legitimacy. To test this framework, the authors field a pre-registered factorial survey experiment in the UK (N = 1,201; 1,198 after exclusions) across three policy domains (tax, welfare, bail) with five vignette conditions: a human baseline, an AI-benefits condition, and three conditions adding a risk framing (dependency, contestability, or assessability) to the benefits text. The outcomes are institutional trust, perceived loss of control, and AI delegation preference, analyzed with linear and ordinal mixed-effects models. The AI-benefits condition raises trust (b = 0.51) while also raising perceived loss of control (b = 0.36); all three risk conditions sharply reduce trust (b around -0.45 to -0.55), increase loss of control (b around 1.2-1.3), and shift preferences toward less AI (odds ratios around 0.03). A pretest (N = 301) with manipulation checks confirms that the risk texts move the corresponding perceptions, though with substantial cross-loading across the three checks.

Significance. If the causal interpretation is clean, this is a strong contribution to the literature on AI governance and public opinion: the study is pre-registered with a power analysis and falsifiable success criteria; the sample is large and stratified; the mixed-effects models are pre-specified with tight confidence intervals; and code and data are posted on OSF for replication. The consistent pattern across three outcomes and the large, precisely estimated effects make the core empirical pattern credible. The principal-agent framing is a useful conceptual contribution, and the 'failure-by-success' hypothesis is falsifiable and policy-relevant. Two issues are load-bearing for the central claims, however: the risk vignettes embed explicit evaluative conclusions that closely paraphrase the dependent variables, and the paper's own domain-specific tables contradict the asserted robustness across policy domains. Both are fixable but require substantive revision of the causal framing and the robustness claims.

major comments (3)
  1. [Section 3; Supplementary B.1; Supplementary E (Tables 14-16)] The central causal claim that awareness of the three principal-agent tensions causes the observed drops in trust and perceived control is not uniquely identified, because the risk vignettes directly assert the outcome constructs. The dependency text states that 'the state has become dangerously dependent on AI' and that 'the authority is now virtually forced to trust the AI'; the contestability text states that 'the human becomes a mere rubber stamp' and that 'What was intended as progress turns into a dehumanised bureaucratic apparatus'; the assessability text asserts that 'independent verification is simply not possible.' Because these phrases are close paraphrases of the dependent variables (loss of control, governmental competence, and trustworthiness), the measured effects may reflect message agreement or demand effects rather than citizens' inferences from the structural features of delegation. The pretest manipulation checks (Tables 14-16) do not resolve this: each risk condition moves not only its target check but also the other two checks in the same negative direction (for example, the dependency condition raises the transparency check by 0.83 and lowers the contestability check by -0.69), so the conditions are not clean operationalizations of single tensions. A concrete way to address this would be to re-estimate the effects with vignettes that describe the structural features without evaluative conclusions, or to reframe the claims explicitly as effects of communications about these tensions.
  2. [Supplementary D.2 (Tables 8-9); Sections 1 and 5] The introduction and Section 5 state that the effects are 'robust across all tested policy domains,' but the paper's own domain-specific models contradict this claim for the welfare domain. In Table 8, the welfare AI-Dependency coefficient on trust is -0.24 (95% CI [-0.49, 0.01], p = 0.056) and the welfare AI-Contestability coefficient is -0.20 (95% CI [-0.44, 0.05], p = 0.114), both statistically indistinguishable from zero at the conventional level. Similarly, the claimed robustness of the AI-benefits effect on loss of control is not supported in the welfare domain, where Table 9 reports 0.23 (95% CI [-0.01, 0.46], p = 0.056). The cross-domain robustness claims should be qualified to the outcomes and domains where the models actually show consistent effects, or the domain heterogeneity should be explicitly modeled and discussed.
  3. [Section 5 (pretest analysis); Section 4 (manipulation check)] The pretest analysis is offered to 'substantiate the processes underlying these results,' and the text asserts that the manipulation-check responses 'precede reductions in institutional trust and increases in perceived loss of control.' However, the reported pretest models (Tables 14-16) estimate only treatment effects on the three perception checks; they do not estimate the relationship between the checks and the outcome variables, and the pretest and the main study are separate samples. The main study, in turn, includes only a domain-recall comprehension check rather than checks of the three tension perceptions. The mediational or temporal 'precede' claim is therefore not supported by the reported analyses; it should be estimated directly (for example, with the main design including tension checks and outcome items in the same sample) or removed.
minor comments (6)
  1. [Section 3] The design section says respondents are randomly assigned to 'one of four conditions,' but five conditions are then enumerated; the count should read five.
  2. [Section 2.2] The sentence 'According to Miller Miller (2005, p. 205f.)' contains a duplicated surname and should be corrected.
  3. [Section 5] The claim that '45.4% of respondents in the human condition report feeling a lack of control (above the scale midpoint)' uses exactly the value previously reported as the share preferring 'More AI' in the human condition, and no distribution for the dichotomized control-loss variable is reported; given the human-condition mean of 4.14 on the 1-7 scale, roughly 54% would be expected above the midpoint, so the 45.4% figure should be verified.
  4. [Supplementary Tables 10 and 13] Tables 10 and 13 are both titled 'Dependent variable is loss of control' but report cumulative-link odds ratios for the ordinal AI-preference outcome; the titles should be corrected to match the models.
  5. [Abstract; Section 6] The abstract and conclusion present the 'failure-by-success' dynamic as an empirical finding, but the design is a static comparison of vignette conditions and does not manipulate the temporal sequence of early success followed by risk awareness; the conclusion acknowledges this ('we could not survey future respondents directly'), and the framing should consistently treat the temporal dynamic as an interpretation rather than a tested result.
  6. [Throughout] The manuscript contains numerous typos and missing spaces (for example, 'pre-registeredourdesignandhypotheses' in Section 1), and the notation for coefficients is inconsistent (b vs. beta) in Section 5; a careful copyedit is needed.

Circularity Check

1 steps flagged · score 5.0 of 10

Risk-condition vignettes embed the outcome (loss of control), so the PAT risk-tension predictions are partly circular by construction; the AI-benefit control-loss result is independent.

  1. self definitional [Supplementary Information B.1 (AI Benefits + Dependency and AI Benefits + Contestability vignettes, tax case); outcome measures in Section 4]
    "Experts are now warning that the state has become dangerously dependent on AI. ... The authority is now virtually forced to trust the AI, even if its decisions appear flawed or non-transparent. ... the human becomes a mere rubber stamp for what the machine proposes ... What was intended as progress turns into a dehumanised bureaucratic apparatus."

    The perceived-control outcome is measured with items such as 'In this country, we are losing control over the important decisions of government' and 'decisions that affect my life are made by anonymous forces beyond my control.' The dependency and contestability vignettes assert these same conclusions: the authority is 'virtually forced to trust the AI' and the human reviewer is a 'mere rubber stamp' in a 'dehumanised bureaucratic apparatus.' The comparison of these conditions against the human baseline therefore builds the outcome into the treatment; the observed drop in perceived control is, at least in part, the respondent repeating the vignette's conclusion rather than drawing an inference from the structural delegation features.

full rationale

Most of this paper is not circular: it is a pre-registered factorial survey experiment with no fitted parameters, no formal derivation, and no load-bearing self-citation. The authors' earlier work on an 'AI penalty' is cited only as corroborating context (Sections 2.2 and 6), not as evidence for the reported effects. The AI Benefits condition, which contains only positive efficiency claims, independently shows that even favorable AI framing reduces perceived control (b=0.36, p<0.001); that specific result is self-contained. The circularity is limited to the three PAT risk conditions: the vignettes do not merely vary a structural feature but explicitly conclude that the state is dangerously dependent, that humans are rubber stamps, and that the result is a dehumanised apparatus—textual assertions that are near-identical to the measured 'loss of control' items. Consequently, the headline claim that 'awareness of each of the tensions identified through PA theory has the expected corrosive effects on both institutional trust and perceived loss of control' is partly forced by the wording of the treatments rather than by citizens' inferences from delegation structure. The pretest manipulation checks confirm the texts move the target perceptions, but they also cross-load on the other checks, so they cannot rescue the specificity of the operationalization. Score 5: partial, construction-level circularity for the risk-condition hypotheses; the AI-benefit finding and the overall empirical design remain non-circular.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The central claim rests on four domain assumptions: the public as principal, AI as an agent without intentionality, the tripartite structure of delegation tensions, and the external validity of vignette responses. No free parameters are fit, and no new entities are introduced.

assumptions (4)
  • domain assumption The public can be modeled as the ultimate principal in the delegation relationship between governments and AI.
    Section 2.3 treats citizens as the principal whose perceived control carries the legitimacy consequences of AI delegation. This extends canonical PAT, which usually models institutional actors, and is not independently validated.
  • domain assumption AI systems can be treated as agents in the canonical principal-agent model despite lacking intentionality and self-interest.
    Section 2.2 maps AI onto the six canonical PAT assumptions, reinterpreting misalignment as technically induced rather than motivational. The paper argues by analogy; no formal model is given.
  • domain assumption Assessability, dependency, and contestability are the structurally relevant dimensions of AI delegation.
    These three tensions structure the entire experimental design. The framework is justified theoretically but is not shown to be exhaustive or independent.
  • domain assumption Responses to hypothetical vignettes approximate how citizens would respond to real AI deployments.
    Stated limitation in Section 6: external validity may be constrained and actual responses could differ. This assumption is load-bearing for the 'failure-by-success' forward-looking interpretation.

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Pith. "Pith review of Artificial Intelligence in Government: Why People Feel They Lose Control." pith.science (2026). https://pith.science/paper/NZN6ROT6

@misc{pith2026250501085,
  author       = {Pith},
  title        = {Pith review of: Artificial Intelligence in Government: Why People Feel They Lose Control},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NZN6ROT6}},
  note         = {Machine review of arXiv:2505.01085}
}
read the original abstract

The use of Artificial Intelligence (AI) in public administration is expanding rapidly, moving from automating routine tasks to deploying generative and agentic systems that autonomously act on goals. While AI promises greater efficiency and responsiveness, its integration into government functions raises concerns about fairness, transparency, and accountability. This article applies principal-agent theory (PAT) to conceptualize AI adoption as a special case of delegation, highlighting three core tensions: assessability (can decisions be understood?), dependency (can the delegation be reversed?), and contestability (can decisions be challenged?). These structural challenges may lead to a "failure-by-success" dynamic, where early functional gains obscure long-term risks to democratic legitimacy. To test this framework, we conducted a pre-registered factorial survey experiment across tax, welfare, and law enforcement domains. Our findings show that although efficiency gains initially bolster trust, they simultaneously reduce citizens' perceived control. When the structural risks come to the foreground, institutional trust and perceived control both drop sharply, suggesting that hidden costs of AI adoption significantly shape public attitudes. The study demonstrates that PAT offers a powerful lens for understanding the institutional and political implications of AI in government, emphasizing the need for policymakers to address delegation risks transparently to maintain public trust.

Figures

Figures reproduced from arXiv: 2505.01085 by the authors.

Figure 1
Figure 1. Density and bar plots for all three dependent variables with the [PITH_FULL_IMAGE:figures/full_fig_p014_1.png] view at source ↗

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.