{"id":"f03ead2d-1978-4d2b-99f8-0ffd3fb8b702","arxiv_id":"2607.21757","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"People trusted AI agents they helped co-design even when the agents' answers were systematically more uniform, more decisive, and less concrete than their own.","lead":"A study of 12 people who co-designed their own AI preference agents for home energy found they mostly trusted the agents, even though independent checks showed the agents often misrepresented them. This suggests that involving people in building AI agents can create confidence in tools that are systematically misaligned with their real views.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"No non-participatory control arm means the central claim that co-design itself drives overtrust is underdetermined; observed trust may reflect generic LLM personalization or the Barnum effect.","rationale":"The paper is a thoughtful qualitative study, and the proposed 'overtrust engine' is a plausible conceptual mechanism with clear components. However, the title and abstract make a causal claim: participation and process transparency 'may drive' overtrust. The evidence, as the reader notes, lacks any non-participatory control. Section 5.2.2 interprets the perceived/independent alignment gap as driven by co-design dynamics, but all four listed components—limited testing, Barnum effect, positivity bias, social desirability—could equally arise from interacting with any personalized LLM agent, especially on unfamiliar topics. The study's design means participants always saw ultimately refined, well-aligned responses during the co-design interview, so high perceived alignment may be an artifact of the refinement loop rather than of participation per se. This is a genuine gap in the causal argument, not a demand for a different research philosophy. The proposed control experiment would directly test whether the overtrust gap is larger after co-design than after passive receipt of a comparable agent. Given that the paper currently supports a weaker claim—that overtrust can occur in this co-design process—the verdict should remain conditional rather than accept the stronger causal mechanism as established. I credit the paper for clear limitations and honest reporting, but the central inference needs the additional comparison arm before the 'overtrust engine' can be treated as specific to co-design.","tokens_in":28619,"tokens_out":3820,"duration_ms":46626,"concrete_test":"Run a between-subjects experiment with N≈60–120 per arm using the same background survey and validation scenarios: (A) full co-design interview as in the paper; (B) no-participation control where participants receive the automatically generated provisional agent description plus agent responses to the same test scenarios, with no ability to edit or refine; (C) ideally, a non-personalized generic LLM persona. Measure perceived representation (the A1 item 'final version... did a good job') before and after seeing the validation scenarios, and independently score agent-human alignment blind to arm. If perceived trust minus independent alignment is not significantly larger in arm A than arm B, the overtrust engine cannot be attributed to co-design; if arm C also shows high trust, the effect is generic LLM personalization rather than participation.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The central claim—that participation and process transparency can act as an 'overtrust engine'—requires that the co-design process itself, not generic LLM persona behavior or the study structure, elevates perceived trust relative to independently assessed alignment. Section 5.2.2 lists limited testing, the Barnum effect, positivity bias, and social desirability as components of this engine, but every one of these could operate for a non-participatory personalized agent: a participant who receives a survey-derived persona and a few well-aligned example responses may also overestimate fidelity, especially on unfamiliar energy scenarios (Section 5.1). The study lacks a comparison arm in which participants evaluate an agent built from the same background data without co-design or process transparency, so the observed gap between perceived and independent alignment cannot be causally attributed to participation. The author's own limitation section (5.1) acknowledges limited participant control, which weakens the 'participation' component further: if participants had little genuine steering, then 'overtrust' may be an artifact of the directed process or of the LLM's confident, homogeneous outputs rather than of co-design. This is not a claim of internal inconsistency—the qualitative observations are plausible—but it is the load-bearing inference of the paper and it is not secured by the current design.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper reports a primarily qualitative study (N=12) in which participants co-designed LLM-based personal preference agents for household energy decisions via a background survey, a co-design interview, and a validation survey. Participants generally reported that their agents represented them well and expressed trust, while independent scenario-based validation showed mixed alignment and revealed that agent responses were more homogeneous, decisive, and abstract than human responses. The paper argues that participation and process transparency can function as an 'overtrust engine': a combination of limited testing, the Barnum effect, positivity bias, and social desirability that promotes trust while concealing systematic misalignment. The author proposes that alignment should be viewed not as a fixed state but as enacted through the co-design process, and discusses implications for research and deployment.","tokens_in":28905,"tokens_out":3975,"duration_ms":46902,"significance":"If the proposed mechanism is accepted, the paper makes a useful and timely contribution to the participatory AI and LLM-agent literature. Its strengths are its honesty about limitations, its explicit engagement with preference plasticity, the qualitative richness of the interviews, and the clear articulation of an aggregate-invisibility problem: systematic biases in agent outputs may be invisible to any individual user while producing structural consequences at scale. The paper does not overclaim statistical generalization and appropriately labels its quantitative comparisons as descriptive. The main value is conceptual: it offers a testable hypothesis that co-design and process transparency may increase trust without increasing objective alignment. However, the central causal interpretation is underdetermined by the design, and the paper would need to be reframed or supplemented before the 'overtrust engine' can be regarded as established rather than as a plausible interpretive hypothesis.","major_comments":[{"comment":"The central claim that co-design/process transparency drives overtrust lacks a non-participatory control condition. Every mechanism invoked in Section 5.2.2 — limited testing, Barnum/Forer effects, positivity bias, social desirability, and the IKEA effect — could plausibly operate for any personalized LLM agent even without co-design. A participant receiving a survey-derived persona and a few well-aligned example responses may likewise overestimate fidelity. The paper's own Section 5.1 acknowledges that participants' genuine steering was 'reasonably limited,' which further weakens the attribution to co-design specifically. The observed gap between perceived and independently-assessed alignment is consistent with the proposed mechanism, but it does not distinguish it from generic LLM personalization effects. This is load-bearing because the abstract and conclusions assert that participati","section":"Abstract; Section 5.2.2"},{"comment":"The 'overtrust engine' is introduced as a 'powerful combination' of four or five mechanisms, but the study does not contain evidence that these mechanisms combine, that they are produced by co-design rather than by the study procedure, or that they jointly constitute a single engine. The qualitative data illustrate each mechanism in isolation, but the inference that co-design 'produced the conditions through which alignment came to be perceived' goes beyond what the data can support. In particular, participants never saw the independent validation results, so their continued trust may reflect an artifact of the study's staged feedback design rather than a stable property of participatory processes. The paper should either present the overtrust engine as an explicitly exploratory model requiring dedicated testing, or provide evidence that the mechanisms co-occur and interact as claimed.","section":"Section 5.2.2; Figure 5"},{"comment":"The 'independent validation' is presented as a benchmark against which perceived alignment is compared, but the paper itself acknowledges in Section 5.2.2 that human preferences on unfamiliar topics are plastic and context-dependent. This undercuts the status of the validation survey as a stable ground truth. The systematic character of agent outputs (homogeneity, decisiveness, abstractness) is less vulnerable to this objection, but the quantitative gap between perceived and independent alignment is contingent on the one-shot survey responses. The author partially addresses this by saying the issue is not that perceived alignment was high while 'real' alignment was low, but the subtlety is not consistently maintained in the abstract and conclusions, which speak of 'mixed human-agent alignment' and 'independent validation.' The distinction should be carried through the entire framing, and","section":"Section 5.2.2; Section 4.5"}],"minor_comments":[{"comment":"The caption states 'Scenario 3 not included as it has a numerical response,' but the text later describes scenario 3's numeric outcomes in detail. Clarify that the figure excludes it for scaling reasons, while the text discusses it separately.","section":"Section 4.5, Figure 3 caption"},{"comment":"The thematic analysis was conducted by the author alone with no mention of inter-coder reliability or independent audit. For a study whose central claims depend on interpretive coding, a brief statement on coding checks or member checking would strengthen transparency.","section":"Section 3.2"},{"comment":"The mitigation suggestions are reasonable but are presented as if they follow directly from the findings. Several, such as 'framing the agent as a well-acquainted advisor,' are not tested in this study and should be labeled as speculative design directions rather than evidence-based recommendations.","section":"Section 5.4"},{"comment":"The data availability statement says no data will be shared. This is understandable given the personal nature of the data and ethics approval, but for a qualitative study with small N and interpretive coding, it limits readers' ability to assess the analysis. At minimum, the author could provide the full coding framework (already in S8) with more extensive anonymized quote excerpts than are currently included.","section":"Section 8"}],"recommendation":"major_revision","confidential_remarks":"The paper is honest and well-contextualized, but the central causal claim is not secured by the design. I would not reject it, because the conceptual contribution is valuable and the author explicitly acknowledges many limitations. However, the lack of a control condition and the interpretive leap to an 'overtrust engine' need to be addressed, either by adding evidence or by explicitly reframing the contribution as hypothesis generation. The paper would also benefit from a clearer distinction between 'perceived' and 'independent' alignment throughout the abstract and conclusions."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The real contribution here is the gap between perceived and independently-assessed alignment: participants who co-designed a preference agent reported high trust and fidelity even when fresh-scenario validation showed mixed, homogeneous, and systematically skewed agent outputs. That demonstration is worth having, and the \"overtrust engine\" framing is a useful way to package known effects (Barnum, IKEA, positivity bias, limited testing) into a mechanism that future work can test.\n\nWhat the paper does well: it is honest and well-documented. The method is laid out in detail, with reporting guidelines followed, limitations stated openly, and claims hedged. The observation that agents were more homogeneous, more decisive, and more abstract than the humans is a concrete addition to the LLM-persona critique. The co-design interview itself is also an interesting data-collection method, and the author is careful not to treat the validation survey as a stable ground truth given preference plasticity.\n\nThe soft spot is the one the stress-test identifies, and it is load-bearing. The title says participation may drive overtrust, but there is no non-participatory control arm. Every component of the proposed engine — Barnum effect, positivity bias, social desirability, confidence in a personalized-sounding output — could operate with a survey-derived persona the user never shaped. The paper's own limitation section (5.1) concedes that participant control was reasonably limited, which further blurs the \"participation\" component. The author is transparent about this, but the central inference is nevertheless underdetermined. Minor weaknesses: a single proprietary LLM, a single researcher doing the coding with no inter-rater reliability check, and no data sharing due to ethics. For a small qualitative study these are acceptable, but they cap the confidence one can place in the specifics.\n\nWho this is for: people working on LLM-based preference simulation, participatory AI, and human-agent trust. It is a hypothesis-generating paper, not a demonstrated causal effect, and should be read and cited as such.\n\nRecommendation: send it to peer review. The study is carefully done, the question matters, and a good referee can push for either a softened framing or a follow-up with a non-participatory comparator. Worth engaging, not worth treating as settled.","headline":"A careful qualitative study with a plausible but not yet secured causal claim: co-design may indeed breed overtrust, but the design cannot isolate participation from generic LLM personalization.","tokens_in":29350,"tokens_out":1964,"would_cite":true,"duration_ms":23699,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Co-designing an LLM-based preference agent can build trust while hiding systematic errors, because the process itself—not just the model—creates the feeling of being represented.","keywords":["LLM agents","co-design","participatory AI","preference simulation","algorithmic fidelity","overtrust","home energy","alignment"],"falsifier":"A direct test would compare one group that co-designs an agent with a matched group that receives an equally personalised agent built without their participation; if trust and perceived fidelity are the same in both groups, the overtrust engine is not specifically driven by co-design. A second check: if independent alignment turns out high for co-designed agents on familiar, well-covered topics, the claim of systematic misalignment would be weakened.","tokens_in":28510,"feed_emoji":"🤖","tokens_out":6819,"duration_ms":67357,"temperature":0.7,"pith_summary":"The paper asks whether co-designing a personal LLM-based preference agent with the person it represents actually makes the agent more accurate. In a study of 12 UK participants building agents for household-energy decisions, people engaged enthusiastically and mostly concluded their agents represented them well. But independent validation on new scenarios showed mixed alignment: agent responses were more homogeneous, more decisive, more abstract, and never chose the \"neither\" option that humans sometimes did. The author argues that participation and process transparency can act as an \"overtrust engine\"—a combination of limited testing, the Barnum effect, positivity bias, and social-desirability bias—that produces trust while hiding misalignment visible only in aggregate. If this is right, co-design's main effect may be to make users feel well represented rather than to make agents accurate, which matters wherever such agents are used to stand in for human preferences in research, policy, or markets.","feed_headline":"Co-design may fuel overtrust in AI agents","feed_subtitle":"In a 12-person study, users trusted co-designed agents even when independent tests showed systematic misalignment.","key_machinery":"The central object is the co-designed personal agent description, a second-person persona built from survey data and interview refinements. The named mechanism is the \"overtrust engine\": the author's term for the combined dynamics—testing only on scenarios that are refined until they look aligned, Barnum-effect acceptance of general statements as personal, positivity and salience bias, and the IKEA-effect-style investment in something one helped create—that turn participation and process transparency into a source of confidence rather than scrutiny. This mechanism carries the paper's argument because it explains the gap between participants' high perceived fidelity and the independent valida","core_discovery":"The paper's central claim is that participation and process transparency in co-designing an LLM-based preference agent function as an \"overtrust engine\": they make the user feel the agent represents them accurately while hiding systematic misalignment that is only visible at the group level. In the reported study, 12 participants co-designed agents for household-energy decisions through a background survey, an interview, and a validation survey; 10 of 12 strongly agreed the final agent did a good job representing their preferences, yet independent scenario testing showed mixed human-agent alignment, with agents never choosing the neutral options humans sometimes chose, and being more homogen","pith_inferences":["If the paper is right, the same overtrust engine should appear in other domains where people hold weakly formed preferences, such as personal finance or health decisions; a controlled replication there would test the mechanism's generality.","Because the systematic biases only show up in aggregate, a practical safeguard would be a 'diversity dashboard' that routinely compares agent-response distributions with human-response distributions on probe scenarios, flagging homogenisation before deployment.","The study's qualitative design cannot separate the participation effect from the Barnum effect; a matched non-participatory personalisation arm would settle whether co-design adds overtrust beyond generic personalised LLM output.","Researchers who use co-designed agents as stand-ins for human samples should treat participants' self-reported fidelity as evidence about the relationship built by the process, not as evidence about predictive accuracy."],"forward_implications":["People who co-design a preference agent are likely to overestimate how well it represents them, even when independent validation shows mixed accuracy.","Agent outputs in this setting tend to be more homogeneous, more decisive, and more abstract than the human responses they stand in for, so simulations built this way will flatten the diversity of human preferences.","The misalignment is invisible from any single user's vantage point; it can only be detected by comparing many agents' responses with many human responses.","Deployed at scale, overtrusted agents could skew energy, research, and policy decisions toward model defaults while each user believes their own interests are being served.","Co-design still provides value—it keeps humans involved, lets misrepresentations be challenged, and is a rich data-collection method—but it does not by itself ensure alignment; ongoing validation and human oversight are prerequisites."],"fun_headline_variants":["Co-design can make users overtrust AI agents","Participation fuels overtrust in AI preference agents","Co-designed AI: trust rises, alignment doesn't","12-person study: co-design breeds overtrust in AI"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"The load-bearing premise is that co-design itself—not generic LLM persona behaviour or the study's format—causes the elevated trust, and the study did not include a control condition with equally personalised agents that participants did not shape.","fun_headline_variants_meta":{"raw":{"variants":["Co-design can make users overtrust AI agents","Participation fuels overtrust in AI preference agents","Co-designed AI: trust rises, alignment doesn't","12-person study: co-design breeds overtrust in AI"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000291,"raw_usage":{"total_tokens":1505,"prompt_tokens":679,"completion_tokens":826,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":423,"completion_tokens_details":{"reasoning_tokens":764}},"tokens_in":423,"tokens_out":826,"duration_ms":8574,"temperature":1.0,"reasoning_tokens":764,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-01T06:45:11.530008+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"A direct test would compare one group that co-designs an agent with a matched group that receives an equally personalised agent built without their participation; if trust and perceived fidelity are the same in both groups, the overtrust engine is not specifically driven by co-design. A second check: if independent alignment turns out high for co-designed agents on familiar, well-covered topics, the claim of systematic misalignment would be weakened.","supporting_citations":[],"review_version":1}