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REVIEW 3 major objections 4 minor 139 references

Stakeholder Participation for Responsible AI Development: Disconnects Between Guidance and Current Practice

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The stakeholder engagement that AI companies already do is largely disconnected from the participation that responsible-AI guidance asks for, so it is not advancing responsible AI.

desk verdict A transparent, well-scoped empirical study that will be useful to the rAI governance community; the headline claim is plausible but slightly overreaches what the instrument can measure. read the letter →

arxiv 2506.09873 v1 pith:DDCWEBLG submitted 2025-06-11 cs.SE cs.AIcs.HC

Emma Kallina , Thomas Bohné , Jat Singh This is my paper · ORCID
classification cs.SEcs.AIcs.HC
keywords stakeholderinvolvementresponsibleAIparticipatorydesigndevelopmentpolicymulti-stakeholdergovernancepublicparticipationco-design
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

Responsible-AI guidance increasingly tells developers to involve stakeholders, but this study suggests that the participation already happening in industry is mostly the wrong kind. The authors reviewed 56 guidance documents from 29 organisations and identified five expected benefits of stakeholder involvement: rebalancing decision power, understanding the social context of a system, anticipating harms, building public trust, and enabling outside scrutiny. They then surveyed 130 AI practitioners and interviewed 10, finding that real-world involvement is driven by customer value, usability, and compliance, centres on end-users, domain experts, and legal teams, and usually happens late with little decision authority. Comparing the two, the paper concludes that current practices can barely deliver the benefits guidance promises, and that commercial incentives actively discourage more responsible participation. The result matters because regulation is starting to require participation, and guidance that names it generically can be satisfied without meaningful change.

What carries the argument

The analytical engine is a two-sided comparison: a thematic analysis of 56 guidance documents from 29 organisations that yields five benefit themes for stakeholder involvement, and a mixed-method picture of industry practice built from an online survey of 130 AI practitioners and 10 semi-structured interviews. The bridge between the sides is Table 2, which maps each benefit theme—rebalancing decision power, detailed understanding of the socio-technical context, improved risk anticipation, increased public understanding and trust, and enabling public scrutiny and monitoring—to observed practitioner behaviour and assigns an estimated contribution level. Stakeholder involvement (SHI) is defined broadly as engaging people with an interest in, or affected by, an AI system at points in its lifecycle; the paper contrasts this with the 'traditional' SHI of agile and user-centred design, which focuses on customers and usability.

What would settle it

A representative audit of commercial AI projects that measures whether affected non-users and the public are involved before system objectives are fixed, and whether their input changes those objectives, would settle the claim; if a substantial fraction of projects show such early, decision-relevant participation, the claimed disconnect is overstated.

Watch

Extended reading notes

Core claim

The central claim, stated in §4.3, is that current stakeholder-involvement practices in commercial AI development are not able to contribute to responsible-AI efforts: the benefits that rAI guidance associates with participation are largely beyond what current practices can achieve. Guidance treats participation as a way to shift agency toward affected communities and to let them shape system objectives; practice treats it as a way to find out what customers want, to make an interface usable, and to check legal boxes. As a result, the people most likely to be harmed—affected non-users, marginalised groups, the general public—are rarely involved even when developers know they will be impacted, and when they are involved it is late and consultative rather than early and decision-making. The paper maps each of the five guidance benefits against its practitioner findings and estimates that current practice contributes 'very low' to two of them, 'medium with limited scope' to two, and 'low with limited scope' to one.

Load-bearing premise

The load-bearing premise is that a self-selected sample of 130 survey respondents and 10 interviewees, recruited through the authors' networks and an online participant platform with descriptions mentioning stakeholder involvement, stands in for commercial AI development generally, and that the authors' Table 2 ratings validly measure how much current practice supports each benefit.

Editorial extensions

If this is right

  • Harms to people outside the customer base will keep going unanticipated, because the groups least involved are exactly the groups most likely to be harmed, even when developers know those groups are affected.
  • Guidance that mentions participation generically is insufficient; it must specify early involvement, who is included, and who holds decision power, otherwise usability testing can satisfy it.
  • Legal and regulatory pressure is the most promising lever, because practitioners already prioritise compliance-driven involvement; tying participation to legal requirements could shift the practice.
  • A clearer vocabulary that separates participatory development from public participation, expert consultation, and public oversight would help prevent customer testing from being labelled as responsible participation.
  • Tools and concrete methods are underused, so guidance with actionable techniques could raise the quality of participation.

Reading between the lines

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

  • This inference goes beyond the paper: the five benefit themes could double as an audit rubric, letting an organisation score its participation practice against the guidance and track whether changes move the score.
  • This inference goes beyond the paper: because the sample skewed toward practitioners already interested in participation, the gap in the broader industry is likely at least as large as the paper measures, not smaller; the paper's own limitations point in the same direction.
  • This inference goes beyond the paper: if the paper is right that legal requirements are the lever, a testable prediction follows—jurisdictions that impose participation duties should show a measurable rise in early involvement of affected non-users, while purely voluntary guidance should not.
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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 / 4 minor

Summary. This paper investigates whether stakeholder involvement (SHI) as currently practiced in commercial AI development can deliver the benefits that responsible AI (rAI) guidance attributes to SHI. The authors thematically analyze 56 guidance documents from 29 organizations, identify five intended benefits, and then compare them with data from an online survey (n=130) and semi-structured interviews (n=10) with AI practitioners. They find that SHI in practice is driven mainly by commercial and compliance concerns, concentrates on revenue-critical stakeholders, uses late-stage low-agency methods, and is discouraged from expanding by internal and commercial agendas. On this basis they argue that current SHI practices are 'largely not able to contribute' to rAI efforts, and they propose guidance, terminology, regulatory, and research interventions.

Significance. If the directional finding is accepted, this is a valuable contribution: it challenges the assumption that familiar SHI practices automatically advance rAI, and it provides a concrete corpus of guidance benefits plus practitioner-reported patterns that can inform regulation and future participatory AI research. The authors transparently report demographic characteristics, statistical tests, and limitations, and the mixed-method design is appropriate for the research questions. The main weakness is that the headline claim about the extent of the disconnect goes beyond what the measurement instruments can certify; the evidence supports direction more strongly than magnitude.

major comments (3)
  1. [§4.3, Table 2] The central claim that current SHI practices 'are largely not able to contribute' hinges on the 'Estimated Contribution of Current Practices' ratings in Table 2, but these ratings are presented without a coding protocol, rubric anchors, or inter-rater reliability check. The survey items measure drivers, stakeholder groups, methods, and barriers; they do not directly measure whether any of the five rAI benefits are realized. For example, 'Improved Risk Anticipation' is rated 'Medium With Limited Scope' based on inferences about whose harms are considered, not on any reported outcome. I recommend either softening the central claim to a directional statement ('evidence suggests that current practices are unlikely to realize the benefits...') or adding a transparent scoring procedure with independent raters to support the ordinal ratings.
  2. [§3.2.2, §5] The sample is a convenience sample recruited through the researchers' networks and Prolific, with 99 of 130 participants from Prolific and recruitment materials that mentioned stakeholder involvement. The paper acknowledges a possible SHI-attuned skew and argues that such a sample is useful for exploring bottlenecks. That argument is fair for the direction of the finding—if anything, a SHI-attuned sample would be expected to overstate rAI-aligned practice—but it cannot support a precise population-level magnitude. Phrases such as 'largely not able to contribute' therefore overstate what the sampling design can establish. I suggest framing the result as 'even among practitioners interested in SHI, current practices are misaligned with rAI guidance.'
  3. [§4.2, §4.3] The inference from 'practices are commercially driven and narrow in scope' to 'practices are not able to contribute to the five benefits' is not fully operationalized. Some commercially motivated activities could partially deliver benefits for a limited group of stakeholders (e.g., usability testing with representative users contributing to understanding of socio-technical context, or compliance-driven expert review contributing to risk anticipation). The survey and interviews establish that such benefits are not the goal and are not pursued for affected non-users or the public, but the data do not establish that the benefits are entirely absent even for the stakeholders who are involved. The mapping in Table 2 is a reasonable analytical synthesis, but it should be labeled as such, and the conclusion should be expressed in terms of 'not targeted' or 'narrowly realized' rather than 'not able to contribute.'
minor comments (4)
  1. [Table 2] The arrow '→' before 'Estimated Contribution of Current Practices' and the italic formatting may be lost in some renderings; a legend or verbal label would make the status of these ratings clearer.
  2. [Appendix C, Tables 6 and 7] The headings read 'T-Tests'; these should be lowercase 't-tests,' and the phrase 'impact' should be 'impacted' in the column headings.
  3. [Figure 1] The text refers to blue, orange, and green categories; please ensure the color scheme is distinguishable for color-blind readers or add pattern labels.
  4. [§6.3] The sentence beginning 'Thus, using law to tie rAI-advancing SHI more directly to commercial interests seems a powerful lever' reads as a conclusion from the present data, but the connection between regulation and changed SHI practice is plausible rather than empirically demonstrated here; consider marking it as a hypothesis for future work.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central comparison is empirical and self-contained.

full rationale

The paper's central claim (§4.3) is that current industry SHI practices are 'largely beyond what current SHI practices can achieve' relative to the five rAI benefits. This claim is established by a three-step empirical comparison: (1) a thematic analysis of 56 external rAI guidance documents to derive the five benefits of SHI; (2) a new survey (n=130) and interviews (n=10) reporting practitioners' drivers, stakeholder selection, methods, and conflicts; and (3) a mapping in Table 2 in which the authors estimate each benefit's likely contribution from those reported practices. No formal derivation is involved, and none of the analyzed quantities is defined in terms of the paper's own output. The survey data were collected afresh, the guidance corpus is external to the paper, and the Table 2 estimates are interpretive judgments about the qualitative results rather than parameters fitted to a target outcome. The paper cites its authors' prior work [61, 62] only for general background on SHI frameworks and tool landscapes, not to supply the load-bearing premise that current SHI fails to advance rAI. The sample's SHI-attuned recruitment is acknowledged in §5 and, if anything, biases against the finding of misalignment, so it does not create a circular inference. The main validity concern—that Table 2's 'Estimated Contribution' ratings lack a formal coding protocol or inter-rater reliability check—is a measurement-quality limitation, not a circularity. The direction of the disconnect is supported by the reported frequencies (e.g., only 4% involving the general public despite 28% acknowledging public impact) and by interview accounts of commercial agendas overriding SHI insights. Therefore the paper's comparison is self-contained against external benchmarks and no step reduces by construction to its own inputs.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper is empirical and does not construct a formal model. Its claims rest on the representativeness of the guidance corpus, the truthfulness of self-reports, and the validity of the authors' qualitative mapping between practice and guidance benefits.

assumptions (3)
  • domain assumption The five-theme taxonomy derived from 56 guidance documents is a faithful representation of the benefits that rAI guidance associates with SHI.
    The guidance document analysis is the benchmark for what rAI guidance wants; the authors' manual thematic coding is not independently verified.
  • domain assumption Self-reported survey and interview responses reflect actual SHI practice in commercial AI development.
    The study relies on practitioners' self-reports of drivers, methods, and stakeholders; there is no observational or artifact-based verification.
  • ad hoc to paper The 'estimated contribution' ratings in Table 2 validly measure the extent to which current practices support rAI benefits.
    These estimates are the authors' interpretive synthesis, not direct measurement of rAI outcomes.

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Cite this review

Pith. "Pith review of Stakeholder Participation for Responsible AI Development: Disconnects Between Guidance and Current Practice." pith.science (2026). https://pith.science/paper/DDCWEBLG

@misc{pith2026250609873,
  author       = {Pith},
  title        = {Pith review of: Stakeholder Participation for Responsible AI Development: Disconnects Between Guidance and Current Practice},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DDCWEBLG}},
  note         = {Machine review of arXiv:2506.09873}
}
read the original abstract

Responsible AI (rAI) guidance increasingly promotes stakeholder involvement (SHI) during AI development. At the same time, SHI is already common in commercial software development, but with potentially different foci. This study clarifies the extent to which established SHI practices are able to contribute to rAI efforts as well as potential disconnects -- essential insights to inform and tailor future interventions that further shift industry practice towards rAI efforts. First, we analysed 56 rAI guidance documents to identify why SHI is recommended (i.e. its expected benefits for rAI) and uncovered goals such as redistributing power, improving socio-technical understandings, anticipating risks, and enhancing public oversight. To understand why and how SHI is currently practised in commercial settings, we then conducted an online survey (n=130) and semi-structured interviews (n=10) with AI practitioners. Our findings reveal that SHI in practice is primarily driven by commercial priorities (e.g. customer value, compliance) and several factors currently discourage more rAI-aligned SHI practices. This suggests that established SHI practices are largely not contributing to rAI efforts. To address this disconnect, we propose interventions and research opportunities to advance rAI development in practice.

Figures

Figures reproduced from arXiv: 2506.09873 by the authors.

Figure 1
Figure 1. Key drivers of SHI as reported by survey participants. Colours illustrate the three broad categories discussed in § [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Frequency with which the survey participants indicated that a specific external or internal stakeholder was involved. [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Methods of SHI as indicated by our survey participants. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Country of residence as indicated by the survey participants (single-choice question). [PITH_FULL_IMAGE:figures/full_fig_p016_4.png]
Figure 5
Figure 5. Figure 5: Gender and age distribution of the survey participants as well as the duration of their experience working along the [PITH_FULL_IMAGE:figures/full_fig_p016_5.png]
Figure 6
Figure 6. Figure 6: Industry for which the survey participants are creating AI systems as well as the planned type of deployment of these [PITH_FULL_IMAGE:figures/full_fig_p017_6.png]
Figure 7
Figure 7. Figure 7: Roles in which the survey participants worked along the creation of AI systems (multiple-choice) as well as the [PITH_FULL_IMAGE:figures/full_fig_p017_7.png]
Figure 8
Figure 8. Figure 8: Answers of survey participants when asked who is deciding on external and internal SHI. The finding that the [PITH_FULL_IMAGE:figures/full_fig_p018_8.png]
Figure 9
Figure 9. Figure 9: Answers of survey participants when asked who they expect to be impacted by the system they have been/are [PITH_FULL_IMAGE:figures/full_fig_p019_9.png]
Figure 10
Figure 10. Figure 10: Challenges selected by survey participants, as well as the top three highest ranked challenges. In average, survey [PITH_FULL_IMAGE:figures/full_fig_p019_10.png]

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