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REVIEW 3 major objections 5 minor 1 cited by

AI Recommendations and Non-instrumental Image Concerns

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Being seen using AI makes people ignore accurate advice.

desk verdict A clean pre-registered experiment showing that anticipating a stranger's review of your AI usage reduces following AI advice and performance; the image-concern interpretation is plausible but the treatment also includes an explicit social comparison, so the mechanism is not perfectly isolated. read the letter →

arxiv 2504.19047 v2 pith:ZSH5OOTZ submitted 2025-04-26 econ.GN cs.AIcs.HCq-fin.EC

David Almog This is my paper · ORCID
classification econ.GNcs.AIcs.HCq-fin.EC
keywords AIrecommendationsnon-instrumentalimageconcernshuman-AIcollaborationalgorithmicaversionsocialonlineexperimentadoption
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

This paper argues that a purely social motive—concern about how one is perceived by a stranger, even when that perception carries no monetary or reputational consequences—makes people turn down accurate AI recommendations. In a pre-registered online experiment, participants who were told that a researcher would review their AI usage on a required video call followed AI advice 4.5 percentage points less often and answered 2.7 percentage points fewer questions correctly, despite bonuses tied only to performance. The claim matters because human–AI collaboration often underperforms in field settings such as medicine, courts, and hiring, and this mechanism offers an explanation for why people override algorithms even when doing so is costly. The finding also implies that the social visibility of AI use is part of what determines whether people benefit from these tools.

What carries the argument

The central mechanism is the video-call review: a scripted, one-time interaction in which a researcher will look at the participant's AI-usage rate and compare it with others, while payoffs depend only on the accuracy of the final answer. This design isolates non-instrumental image concerns by making AI reliance observable to a person who can form an impression but has no power over the participant. The two-stage task (initial choice, then AI recommendation with an option to switch) is what makes the behavior measurable: 'using AI' is defined as changing one's answer to match the recommendation, and because the recommendation arrives after the initial choice, the act of following it is unambiguously attributable to the participant. The image-fixed-effects regressions with participant-level clustered standard errors are the statistical vehicle, but the conceptual engine is the contrast between an accuracy-only incentive scheme and a one-time human review.

What would settle it

A control condition with identical instructions except that the AI-usage review is conducted by an automated system with no human observer, while the video call still reveals the bonus, should eliminate or greatly shrink the treatment effect on AI use and accuracy. If the reduction persists in that condition, the paper's attribution to non-instrumental image concerns—rather than to a general effect of being monitored—would be weakened.

Watch

Extended reading notes

Core claim

The paper's central finding is that non-instrumental image concerns—worry about being seen in a certain light by an observer, with no consequences attached to that perception—reduce the use of AI recommendations and lower task performance. In the experiment, all participants completed fifty rounds of image classification, chose an initial answer, saw an AI recommendation, and could switch; a $5 bonus was paid for a randomly selected correct final answer. Everyone also joined a brief video call to learn their bonus, but only treated participants were told that the call would include a review of their AI usage compared with the average participant. This single change reduced AI-following by 4.5 percentage points overall (and by 6.9 points when a switch was possible) and cut final accuracy by 2.7 percentage points; initial-choice accuracy, response times, and effort did not shift. The paper reads this as evidence that the anticipated gaze of another person—not any change in incentives, information, or effort—led participants to sacrifice accuracy to avoid appearing reliant on AI.

Load-bearing premise

The result stands or falls on the assumption that telling participants a researcher will look at their AI use changes nothing except how they feel about being seen using AI—not what they think the task is really about, not what they expect to learn about their own ability, and not a desire to please the experimenter.

Editorial extensions

If this is right

  • In workplaces where coworkers, managers, or clients can see how often someone relies on AI, underutilization of accurate recommendations should be stronger than in the anonymous experiment, because the image stakes are higher.
  • The observed 2.7-point accuracy loss implies that when participants switched away from the AI, they were replacing an 85%-accurate recommendation with their own answers, so reducing image concerns could raise decision quality without changing payoffs or information.
  • Making AI use less observable—for example, by aggregating usage statistics, delaying feedback about following rates, or routing review through an automated system—should increase recommendation following and accuracy.
  • Because minority participants showed larger treatment effects, fairness assessments of AI-assisted decisions should include how people respond to being observed using AI, not only the algorithm's outputs.

Reading between the lines

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

  • Pith editor's inference: the paper's design implies that the image cost is about the act of relying on AI, not about the gender or age of the observer; varying the observer's identity (peer vs stranger, human vs automated) would test whether the effect is specific to human judgment or generalizes to any 'audience.'
  • Pith editor's inference: the result suggests a behavioral explanation for racial gaps in AI adoption beyond access: if minority workers anticipate harsher judgment, they may avoid useful tools even when they are free, a mechanism that could be tested with a stereotype-threat manipulation.
  • Pith editor's inference: the durable digital trace of AI use is the feature that makes avoidance possible; an exact replication with a setting where reliance is harder to attribute (such as a verbal suggestion) would clarify whether the effect is about AI specifically or about any assistance that leaves an audit trail.
  • Pith editor's inference: the paper's self-report measure of discomfort is suggestive but not incentive-compatible; a sharper test would make participants choose between a private and a public AI-use setting with a small monetary trade-off, revealing the shadow price of being seen.
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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 / 5 minor

Summary. This paper reports a pre-registered online experiment (Prolific, N=220) examining whether non-instrumental image concerns reduce the use of AI recommendations. Participants completed 50 rounds of an image classification task with an AI recommendation offered after their initial choice; one round was randomly selected for a performance-based bonus. In the treatment condition, participants were told that a research team member would review their AI usage and compare it with the average participant's behavior during a mandatory end-of-study video call; in the control condition the video call only revealed the bonus outcome. The treatment reduced AI recommendation use by 4.5 percentage points (10%) and final accuracy by 2.7 percentage points (3.3%), with no significant effects on initial choices or response times. The paper interprets these effects as evidence that non-instrumental image concerns cause underutilization of AI, and reports exploratory heterogeneity showing stronger effects for minority participants.

Significance. The paper addresses an important question: why human-AI collaboration underperforms despite accurate AI recommendations. It has notable strengths: the experiment is pre-registered, treatment groups are balanced on observables (Table 1), the main regressions include image fixed effects and participant-clustered standard errors, and the two pre-specified hypotheses (lower AI use, lower accuracy) receive statistically significant support. The distributional shift (first-order stochastic dominance in Figure 1) and the back-of-envelope calculation that treated participants substitute 85%-accurate AI advice with own answers correct only about 25% of the time add credibility to the behavioral result. However, the strong mechanistic claim of non-instrumental image concerns is not uniquely identified by the design, and the paper's defense in Section 2.5 conflates experimenter demand with the intended construct. The underlying behavioral effect is likely real, but a more cautious interpretation or an additional control condition is needed before the mechanism claim can be accepted.

major comments (3)
  1. [Section 2.2 and Appendix A.3] The treatment bundles human observation with explicit comparison-to-average information. Participants in the treatment group are told that the research team member will review their AI usage and compare it to the average participant's behavior (Section 2.2); the control condition contains neither element. The comparison component can operate independently of any social-image channel: it may convey a descriptive norm, activate a reference point, or signal the experimenter's expectations about appropriate AI use. For instance, a participant may infer from the fact that a comparison is being made that heavy reliance on AI is unusual or undesirable, and reduce usage accordingly. The paper's statement in Section 2.2 that 'the key is not the actual comparison but the moment participants anticipate it' does not resolve this confound, because anticipation of comparison can itself trigger norm-based conformity. A design that separates the two components (e.g., a treatment with human observation but no comparison, or a comparison without a human observer) is needed to attribute the effect to non-instrumental image concerns rather than to information about norms. Without such a control, the estimated effect is an upper bound on the image-concern channel.
  2. [Section 2.5] The claim that social-pressure experimenter demand is 'precisely the object of study' conflates two distinct mechanisms. If participants reduce AI use because they infer the experimenter's expectations and want to comply, that is an experimenter demand effect, not a non-instrumental image concern about how they are perceived by peers or by a neutral observer. The experimenter is not a neutral stranger: the participant knows the experimenter designed the study and can potentially evaluate the participant's behavior in light of the research question. The paper even describes the experimenter as 'an authority figure' (Section 2.5). Under that description, approval-seeking is an instrumental response to a perceived authority, with no necessary connection to the real-world construct of image concerns among colleagues, clients, or supervisors. To make the target construct credible, the paper must either argue more carefully that the experimenter's neutral language precludes demand (which it currently does not) or provide auxiliary evidence that the effect is not explained by belief about the experimenter's desired behavior.
  3. [Section 3.3] The self-reported discomfort measure is offered as support for the mechanism, but it cannot discriminate between non-instrumental image concerns and more general evaluation apprehension or discomfort about being monitored. The 55% increase in discomfort (42 vs. 27 participants reporting 'agree' or 'strongly agree') is consistent with the image account, but it is also exactly what one would expect if participants simply dislike being observed by a researcher, regardless of any concern about the researcher's perception of their AI use. Moreover, the second survey question ('I believe I made good use of AI recommendations') is a retrospective self-assessment and may reflect post-treatment rationalization rather than a direct measure of perceived constraint. The paper should acknowledge these limitations explicitly or supplement the survey evidence with a more targeted measure, such as beliefs about what the researcher would think of them for using AI, or an index that separates shame from mere discomfort.
minor comments (5)
  1. [Introduction and Section 2.5] There are several typographical errors: 'ashe-donic motives' should read 'as hedonic motives'; 'asocial pressure' should read 'a social pressure'; and the pre-registration identifier is rendered inconsistently (the acknowledgments cite #220425, while Section 2.2 gives a URL that does not contain this number).
  2. [References] The reference for de Quidt et al. (2018) is incorrect: the title 'Stigma in welfare programs' belongs to a different paper (e.g., Friedrichsen et al. or Celhay et al.), not to the demand-effects review actually cited in the text.
  3. [Section 3.1] The sentence 'Regressing initial correct answers on Response time (controlling for image-level noise and participant fixed effects)...' is confusing: it is unclear whether the dependent variable is initial accuracy or response time, and the direction of the regression should be stated precisely.
  4. [Section 3.2 and Table B.1] The paper would benefit from a discussion of multiple testing for the six pre-registered outcomes and for the exploratory heterogeneity analyses in Table B.1; the reported p-values are not adjusted, and the significant minority interaction is described as 'striking' despite the multiple comparisons.
  5. [Throughout] Replication data and code are not provided; given the simplicity of the design, posting them would strengthen the paper's reproducibility and allow readers to verify the clustered standard errors and the construction of the AI-use measures.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the core treatment effects are measured from randomized outcomes independent of the mechanism construct, and self-citations are background rather than load-bearing.

full rationale

The paper's central results are a pre-registered randomized experiment, not a derivation. The treatment (Section 2.2) informs participants that their AI usage will be reviewed and compared with the average; the outcomes (Section 3.1) are independently measured AI use and accuracy. There is no fitted parameter renamed as a prediction, no structural model whose output equals its input, and no equation making the outcome equivalent to the treatment by construction. The mechanism label 'non-instrumental image concerns' is an interpretation supported by auxiliary evidence (Section 3.3: discomfort and self-assessed quality of AI use), not by definitional equivalence. The only passage that could resemble circular reasoning is Section 2.5's statement that a social-pressure type of experimenter demand effect is 'precisely the object of study'; however, this is an identification and construct-validity claim rather than a circular derivation, because the behavioral predictions and the discomfort measure are separate from the treatment definition. Self-citations to Almog et al. (2025a, 2025b) appear only in the literature review and are not used to justify the experimental design or the inference. Possible concerns about bundled treatment components or experimenter demand are confound/interpretation issues, not circularity, and therefore do not raise the circularity score.

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

The paper introduces no free parameters or invented entities. The central claim relies on four domain assumptions about the experimental manipulation, participant beliefs, conditional comparability, and self-reported mechanism measures.

assumptions (4)
  • domain assumption The video call with a researcher, whose AI-usage review has no monetary or future reputational consequences, successfully induces non-instrumental image concerns in treated participants.
    The paper's identification strategy rests on this. The self-reported discomfort question provides supportive but indirect evidence. Location: Section 2.3 and Section 3.3.
  • domain assumption Participants believed the AI recommendation was 85% accurate as stated.
    The experiment instructs participants that the AI is 85% accurate, which is true for the dataset. The interpretation that rejecting AI is costly depends on participants trusting this number. Location: Section 2.4.
  • domain assumption Initial choices and the composition of switch-available rounds are not differentially affected by treatment, so the conditional analysis is unbiased.
    The paper tests this and finds no treatment effect on initial accuracy or response time, but the assumption is required for the conditional comparison in Column 2 of Table 2. Location: Section 3.1.
  • domain assumption The two self-reported questions about discomfort and AI use effectiveness reflect the underlying psychological mechanism rather than pure experimenter demand.
    The mechanism analysis relies on Likert responses; the paper argues demand is mitigated by between-subjects design and neutral language. Location: Section 3.3 and Section 2.5.

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Cite this review

Pith. "Pith review of AI Recommendations and Non-instrumental Image Concerns." pith.science (2026). https://pith.science/paper/ZSH5OOTZ

@misc{pith2026250419047,
  author       = {Pith},
  title        = {Pith review of: AI Recommendations and Non-instrumental Image Concerns},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZSH5OOTZ}},
  note         = {Machine review of arXiv:2504.19047}
}
read the original abstract

There is growing enthusiasm about the potential for humans and AI to collaborate by leveraging their respective strengths. Yet in practice, this promise often falls short. This paper uses an online experiment to identify non-instrumental image concerns as a key reason individuals underutilize AI recommendations. I show that concerns about how one is perceived, even when those perceptions carry no monetary consequences, lead participants to disregard AI advice and reduce task performance.

Figures

Figures reproduced from arXiv: 2504.19047 by the authors.

Figure 1
Figure 1. CDF on Average Recommendation Use per Participant. [PITH_FULL_IMAGE:figures/full_fig_p013_1.png] view at source ↗
Figure 2
Figure 2. Heterogeneous Treatment Effects by Ethnicity. [PITH_FULL_IMAGE:figures/full_fig_p016_2.png] view at source ↗
Figure 4
Figure 4. I believe I made good use of AI recommendations during this task. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_4.png] view at source ↗

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