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REVIEW 4 major objections 5 minor 105 references

Self-driving technologies need the help of the public: A narrative review of the evidence

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Self-driving technology needs the public's help, and the way to earn it is engagement built around what people actually care about.

desk verdict A practical, well-structured narrative review that turns trust and engagement literature into a usable framework for AV public engagement, but the causal step from survey-measured intentions to engagement outcomes is an extrapolation, not a finding. read the letter →

arxiv 2505.23472 v2 pith:RWYRHLXH submitted 2025-05-29 cs.HC

Jonathan Smith , Siddartha Khastgir This is my paper
classification cs.HC
keywords self-drivingvehiclespublictrustengagementcalibrationinformedsafetytechnologyacceptanceco-creationautomated
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 self-driving vehicles will only succeed if public trust is built through engagement, and that engagement works when it starts from what matters to the public rather than from what engineers think matters. Reviewing evidence on trust, safety-critical technology, and acceptance, it identifies five factors that consistently shape people's intentions: personal benefit, ease of use, enjoyment, attitude, and trust. It proposes a scalable model: begin with proof-of-concept material on those factors, co-create it with stakeholders, and amplify it through direct, stakeholder, and media channels while measuring whether the public moves from passive consumption to informed criticism and advocacy. The reason to care is that a trust failure early in the technology's life could delay the benefits of self-driving vehicles by years.

What carries the argument

The central object is a staged 'calibration' model of public trust, distinguishing knowledge-about and trust-in (before use: benefits, what a self-driving vehicle is, safety, security, ease of use, governance and ethics, challenges and limitations) from knowledge-from and trust-with (during use: experience, using, malfunction, and enforcement bodies). It is powered by three established distinctions: informed safety, which means calibrating expectations with knowledge about system limits; the passive-to-active engagement continuum, which runs from clicks and views to critique and advocacy; and the three Co's of co-creation, namely co-define, co-design, and co-refine. These carry the argument because they turn trust from an abstract attitude into a process that engagement programmes can target, evaluate, and scale.

What would settle it

Run a large, pre-registered longitudinal study in which one group of the UK public receives co-created engagement material built on the five factors and another receives a conventional information campaign, then measure calibrated trust, expectations, and active engagement over at least a year; if the co-created approach does not outperform the conventional one, the paper's central claim about what builds trust would be contradicted.

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Extended reading notes

Core claim

On its own terms, the paper's central claim is that trust in self-driving technology is not primarily a feeling but a consequence of the information and reasons people have access to, so engagement programmes must be organised around the public's concerns if they are to work. Synthesising over 100 studies, it concludes that five factors consistently matter most: performance expectancy, effort expectancy, hedonic motivation, attitude, and perceived trust, and that experts frequently misjudge what the public cares about. It then proposes a staged engagement model: a framework of trustworthy principles, a journey model that calibrates trust from 'knowledge about and trust in' before first contact through 'knowledge from and trust with' during use, co-created content, and a scalable amplification strategy. The end-state it aims for is a public that acts as informed critics and advocates, not merely an accepting audience.

Load-bearing premise

The evidence base is mostly surveys of people who have never ridden in a self-driving vehicle, and the paper assumes those stated intentions and the five priority factors reflect what will actually shape public trust and acceptance once the technology is on the road.

Editorial extensions

If this is right

  • Engagement programmes should start from the public's perspective and the five priority factors, not from technical feature lists, because experts often misunderstand what the public cares about.
  • Co-creating content with stakeholders makes those stakeholders back the final material and actively boost its distribution, which is the foundation for scaling.
  • If engagement is two-way and repeated over time, the public can progress from passive awareness to informed critique and advocacy, rather than remaining an audience to be persuaded.
  • Trust should be calibrated with the right information at the right time in the right format, and it must begin before the public's first ride in a self-driving vehicle.
  • A mismatch between public expectations and actual system capability leads to misuse, disuse, or abuse of the technology, so ongoing educational and legislative calibration is necessary.

Reading between the lines

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

  • The paper does not test its own model against an implemented engagement programme, so an editorial inference is that the model's real test will be whether co-created, factor-based engagement measurably improves calibrated trust in live trials.
  • Because the paper treats trust as dynamic, its five-factor baseline will likely need continuous re-baselining as people gain real experience, which the paper gestures at but does not operationalise.
  • The same symmetrical, co-creative engagement logic could plausibly transfer to other safety-critical emerging technologies, such as AI in healthcare, though that extension is not made in the paper.
  • A testable extension suggested by the paper's 'sites of contention' principle is whether publicly debating real self-driving incidents builds more durable calibrated trust than only distributing polished educational content.
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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

4 major / 5 minor

Summary. The paper is a narrative review, produced under the PAVE UK programme, that argues public trust and acceptance of self-driving vehicles depend on engagement organised around what matters to the public. It develops this argument in three movements: first, a conceptual account of trust and trustworthiness illustrated by case studies on NHS/DeepMind data sharing and UK nuclear power consultation; second, a synthesis of engagement theory covering stakeholder engagement, co-creation, storytelling, and communication practice; and third, a review of survey-based adoption literature that identifies five factors—performance expectancy, effort expectancy, hedonic motivation, attitude, and trust—as the most influential predictors of behavioural intention. The paper concludes with a 'calibrating trust' model (Figure 6), a co-creation framework for content development (Figure 7), and a scaling model for engagement outcomes. The central claim is that engagement material organised around the public's own concerns can deepen conversation, build trust, and move the public toward roles as informed critics and advocates.

Significance. If the central claim is accepted as a design hypothesis rather than as an established empirical result, the paper offers a useful synthesis for practitioners and policymakers working on automated vehicle education. Its strengths include a broad and mostly well-chosen set of sources spanning trust research, public engagement theory, and technology adoption; two instructive case studies that ground the concept of trustworthy process; an explicit acknowledgement that the underlying evidence is dominated by survey-based intention studies; and a concrete, actionable framework with implementation recommendations. The paper also has the virtue of being falsifiable: the proposed model makes specific, testable predictions about the effects of engagement content and format on trust, critique, and advocacy. The value of the contribution therefore depends on how the authors frame the evidentiary status of the model and on how well they document the evidence base on which it rests.

major comments (4)
  1. [Section 4 and Figure 6] The central causal step of the model is not supported by the cited evidence. Section 4 identifies five factors that predict behavioural intention in UTAUT-based surveys, and Figure 6 converts these into sequential knowledge attributes ('Why: Benefits', 'What is a self-driving technology?', 'How safe, secure and easy to use', 'How: Governance and ethics', 'When/where: Challenges and limitations') that engagement programmes should follow. However, the reviewed studies measure stated intention in populations that mostly have never ridden in a self-driving vehicle, as the paper itself notes in 'Limitations in the data' (Section 4) and as Mansoori et al. (2023) document through the dominance of 'Behavioural Intention' as the target variable. No cited study manipulates engagement content or delivery format and then measures trust, critique, or advocacy. The claim that orientating engagement around these five factors 'creates the potential for ever more sophisticated conversations, greater trust, and moving the public into a progressively more active role of critique and advocacy' is therefore a plausible hypothesis, not a finding of the review. I recommend reframing Figure 6 and the accompanying discussion explicitly as a design hypothesis, and adding a concrete evaluation plan (e.g., pilot engagement programmes comparing public-centred versus expert-centred materials, with pre/post measures of trust, knowledge, and advocacy behaviour).
  2. [Section 4, 'Approaching the data: methodology'] The keyword search for the review is not reproducible. The text states that keyword searches for 'Review' or 'Summary' or 'Status' or 'Systematic' or 'Comparison' and 'trust' or 'Acceptance' or 'Adoption' and 'Automated Vehicle' or 'Autonomous Vehicle' or 'Self Driving Vehicle' were undertaken, and that review papers from 2023 and 2024 were given priority, but it does not specify the databases searched, the date of the search, the inclusion and exclusion criteria, the number of records screened, or how the final set was selected. Given that the five-factor framework is the empirical foundation for Figure 6, the absence of a documented protocol makes it difficult for readers to assess whether the selection was comprehensive or biased. I recommend either adding a reproducible search protocol and a PRISMA-style flow diagram, or softening the claim that the review provides a 'robust base data-driven understanding' and presenting the five factors as themes emerging from a purposive, illustrative sample.
  3. [Executive Summary and Section 4] There is an inconsistency between the claim of 'systematic analysis of over 100 studies' in the Executive Summary and the actual methodology described in Section 4. The methodology is a narrative review that prioritises existing review papers, and the two quantitative meta-reviews cited (Rahman et al. 2023, 81 sources; Mansoori et al. 2023, 71 studies) together cover fewer than the claimed 100 unique studies, with unknown overlap. The phrase 'systematic analysis' also overstates the methodological status of the review. The authors should either document how the count of over 100 studies was derived or remove/adjust the claim so that the Executive Summary matches the methods described in the body of the paper.
  4. [References and in-text citations] Several citation errors undermine the reliability of the narrative review. For example, the text attributes the finding that people 'can forget some of their gained trust (or distrust) in automation over time' to reference [11], but reference [11] is a public-relations textbook chapter, while the relevant source appears to be Hunter et al. (2022), listed as reference [10]. Similarly, in Section 5 ('Scalability'), the shift from communication to engagement is cited as ([21], [37], [38]), yet reference [21] is a UN road-safety page and references [37] and [38] are the nuclear consultation evaluation and Winfield and Jirotka (2018), neither of which supports the sentence as cited. The text also inconsistently attributes the concept of 'calibrated trust' to Rempel et al. via reference [6] after introducing it via reference [13] in Section 2. I recommend a systematic verification of all in-text citations against the reference list before resubmission.
minor comments (5)
  1. [Figure 2 caption] The caption 'Figure 2: self-driving technology technology focused model of trust (Khastgir 2019)' contains a duplicated word 'technology technology'; the in-text reference to the underlying work is given as Khastgir et al. (2018) elsewhere, so the year should also be reconciled.
  2. [Section 2] The sentence 'Rempel et al call calibrated trust [6]' is confusing because the concept of calibrated trust was introduced earlier in the same section with reference [13] (Valentine et al. 2021). Please clarify which source actually introduces the term, or remove the redundant attribution.
  3. [Section 3] There is a typographical error in 'the Covind-19 pandemic' on page 21; this should be 'COVID-19'.
  4. [Section 4] The spelling of the author name 'Greisfenstine' in the text does not match 'Greifenstein' in the reference list; please unify the spelling.
  5. [Throughout] The paper alternates between 'self-driving technology' as a singular concept and 'self-driving technologies' as countable systems, sometimes within the same paragraph (e.g., Section 4's discussion of private versus public services). Consistent terminology would improve readability, especially in the model descriptions in Figure 6.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the engagement model is a synthesis of external evidence; the only self-citation (Khastgir et al.'s informed-safety concept) is background input, not a load-bearing derivation.

full rationale

The paper does not fit parameters to data and then predict those same data; there are no equations that reduce an output to an input. The five engagement factors (performance expectancy, effort expectancy, hedonic motivation, attitude, trust) are drawn from external systematic reviews and meta-analyses (Rahman et al. 2023; Mansoori et al. 2023), not from the authors' own prior work, and Figure 6 repurposes them as discussion prompts for engagement. The move from factor identification to the proposed engagement model is an interpretative synthesis of qualitative and quantitative literature, not a derivation that is forced by construction. The paper's central claim, that engagement oriented around what matters to the public can build trust and support more active public roles, is supported by cited external engagement scholarship (Dhanesh 2017; Rempel et al. 2018; Bates et al. 2010) and is explicitly qualified by the acknowledged limitations in Section 4, where the evidence base is described as mostly survey-based and lacking lived experience of self-driving technology. The self-citation to Khastgir et al. (2018, 2019), which includes a co-author of this paper, supplies the 'informed safety' and 'trust in / trust with' vocabulary used in the model, but these concepts are embedded in a wider external evidence base (Lee and See 2004; Valentine et al. 2021; Zhang et al. 2024) and are not used to forbid alternatives or to justify the model's central claims by appeal to authorial authority. No load-bearing step reduces to its own inputs, and the one self-citation is not the basis of the paper's main conclusions. The derivation chain is therefore substantially self-contained, and any circularity burden is minimal.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The paper contains no free parameters or invented entities. It relies on domain assumptions about the transferability of trust and engagement research to self-driving technology, and on an ad hoc literature selection procedure that is not fully disclosed.

assumptions (5)
  • domain assumption The reviewed studies, conducted mostly before hands-on experience with self-driving vehicles, are representative of UK public attitudes.
    The paper builds its five-factor framework from surveys and simulator studies cited in Section 4, but the authors acknowledge participants had little real experience, so generalization to post-deployment public views is an unverified assumption.
  • domain assumption Trust models from other safety-critical domains (healthcare data sharing, nuclear policy) transfer to self-driving technology.
    Case studies in Section 2 are used to draw lessons on transparency and engagement; the paper does not test whether these lessons hold for autonomous vehicles specifically.
  • domain assumption The five factors (performance expectancy, effort expectancy, hedonic motivation, attitude, trust) are the most important drivers of behavioural intention.
    The paper selects these from meta-reviews, but the reviews themselves note many other factors; the selection is justified by 'four far exceed the others' without significance testing.
  • ad hoc to paper The keyword search, prioritising 2023-2024 review papers, captured all relevant evidence.
    The methodology in Section 4 describes broad keyword combinations but provides no protocol, database list, or inclusion/exclusion criteria, so the evidence base is not auditable.
  • domain assumption Public engagement improves trust and acceptance.
    The paper assumes that two-way engagement and co-creation produce calibrated trust, a premise from communication literature; the paper does not provide controlled evidence for this effect in self-driving technology.

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Cite this review

Pith. "Pith review of Self-driving technologies need the help of the public: A narrative review of the evidence." pith.science (2026). https://pith.science/paper/RWYRHLXH

@misc{pith2026250523472,
  author       = {Pith},
  title        = {Pith review of: Self-driving technologies need the help of the public: A narrative review of the evidence},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RWYRHLXH}},
  note         = {Machine review of arXiv:2505.23472}
}
read the original abstract

If public trust is lost in a new technology early in its life cycle it can take much more time for the benefits of that technology to be realised. Eventually tens-of-millions of people will collectively have the power to determine self-driving technology success of failure driven by their perception of risk, data handling, safety, governance, accountability, benefits to their life and more. This paper reviews the evidence on safety critical technology covering trust, engagement, and acceptance. The paper takes a narrative review approach concluding with a scalable model for self-driving technology education and engagement. The paper find that if a mismatch between the publics perception and expectations about self driving systems emerge it can lead to misuse, disuse, or abuse of the system. Furthermore we find from the evidence that industrial experts often misunderstand what matters to the public, users, and stakeholders. However we find that engagement programmes that develop approaches to defining the right information at the right time, in the right format orientated around what matters to the public creates the potential for ever more sophisticated conversations, greater trust, and moving the public into a progressive more active role of critique and advocacy. This work has been undertaken as part of the Partners for Automated Vehicle Education (PAVE) United Kingdom programme.

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Reference graph

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.