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REVIEW 4 major objections 4 minor 82 references

Why Do Decision Makers (Not) Use AI? A Cross-Domain Analysis of Factors Impacting AI Adoption

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Decision-maker adoption of AI is governed by four cross-domain factors—background, model perception, personal consequences, and stakeholder implications—and can be analyzed with an adoption sheet.

desk verdict A plausible but thinly evidenced framework for AI adoption; worth refereeing, but the cross-domain claims need tempering. read the letter →

arxiv 2508.00723 v1 pith:ZNHUNGNK submitted 2025-08-01 cs.HC

Rebecca Yu , Valerie Chen , Ameet Talwalkar , Hoda Heidari This is my paper · ORCID
classification cs.HC
keywords decision-makeradoptionAIhuman-AIdecision-makingsemi-structuredinterviewscross-domainanalysissheetmedicinepublicsector
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

Decision-maker adoption—the voluntary, consistent choice to consult an AI tool—is the missing layer between an organization buying AI and a human relying on its output. The paper claims this choice is governed by four cross-domain factors: the decision-maker's own background, their perception of the model, the consequences they personally face, and what they believe other stakeholders gain or lose. Based on 16 interviews across journalism, law, medicine, and the public sector, the authors propose an AI adoption sheet that turns these factors into diagnostic questions, and show through two case studies why similar tools are adopted in some domains and not others. The value is practical: if the framework holds, developers and policymakers can anticipate uptake before deployment rather than after.

What carries the argument

The AI adoption sheet is the paper's central instrument: a short checklist of questions under the four factors (decision-maker background, model perception, consequences for the decision-maker, perceived implications for other stakeholders). It functions as an analytic lens for comparing use cases across domains and as a prospective tool for developers and policymakers to ask why a given decision-maker would or would not use a tool before it is built or deployed. The case-study charts turn the sheet into per-domain ratings—plus, minus, or tilde—showing how the same use case can get opposite adoption signals in different professions.

What would settle it

Run a larger, pre-registered survey of clinicians, lawyers, journalists, and public-sector workers that codes their stated adoption reasons against the four factors; if a substantial share of decisions is driven by reasons outside the four categories, or if the factors do not predict actual consultation behavior in a longitudinal deployment, the framework's completeness claim fails. A simpler disconfirmation would be finding that e-discovery tools are already routinely adopted by clinicians in a broad sample, since the medicine case study predicts the opposite.

Watch

Extended reading notes

Core claim

The paper's central claim is that before any question of reliance—how often a decision-maker defers to AI—there is a prior choice of adoption, and that choice is systematically shaped by four factors that recur across domains. In journalism, law, medicine, and the public sector, decision-makers weigh their professional experience and personal biases, what they believe about the model's flaws, transparency, and capabilities, the legal, professional, political, and workload consequences they would bear, and the perceived impact on their organization and on the people their decisions affect. The authors use these factors to explain two concrete puzzles: e-discovery tools are adopted for research and analysis in journalism, law, and the public sector but rarely in medicine, while tools that tailor communication to different audiences are used in journalism, medicine, and the public sector but not in law. The explanation, summarized in plus/minus/tilde charts, is that the same factor pushes different ways in different domains—for example, liability concerns and distrust of opaque models weigh heavily against adoption in medicine, while easy verification makes e-discovery attractive in journalism.

Load-bearing premise

The framework rests on 16 interviews—two in law and several with researcher-insiders instead of practicing decision-makers—being representative enough to support conclusions that generalize across entire professions.

Editorial extensions

If this is right

  • Reliance studies that measure how often people defer to AI need to separate adoption from reliance, since non-adoption can masquerade as low reliance.
  • Model developers can use the adoption sheet before deployment to identify domains where a tool will stall because of liability, distrust, or stakeholder concerns.
  • Organizational adoption—a hospital buying a system or a firm mandating a tool—does not imply decision-maker adoption, and the two can be driven by different factors.
  • The same AI capability can face opposite adoption outcomes in different professions, so domain-blind deployment strategies are likely to fail.
  • Because perceptions change with exposure, adoption is not static; early negative impressions can shift as tools improve and organizations support them.

Reading between the lines

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

  • A natural test is to convert the adoption sheet into a structured survey and administer it to a large sample across new domains such as education or finance; if the four factors fail to predict stated adoption, the framework needs revision.
  • The case studies imply that adoption barriers are often about the decision-maker's liability and the inability to verify outputs, not about the raw quality of the model; this suggests that improving verifiability and indemnification may matter more than accuracy gains.
  • The framework could be made dynamic: tracking the same decision-makers over time would test whether exposure, organizational support, and regulatory clarity shift adoption in the direction the interviews suggest.
  • For high-stakes domains, the sheet could be used as a pre-deployment checklist in procurement, turning a research finding into a governance artifact, though the paper itself leaves that empirical validation to future work.
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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 / 4 minor

Summary. This paper argues that decision-maker adoption of AI—defined as the voluntary and consistent consultation of AI tools within a workflow—should be distinguished from organizational adoption and from reliance on AI outputs. Based on 16 semi-structured interviews across journalism, law, medicine, and the public sector, the authors induce four factors that shape adoption: the decision-maker's background, perceptions of the AI model, consequences for the decision-maker, and perceived implications for other stakeholders. These factors are organized into an 'AI adoption sheet' and applied to two cross-domain case studies: e-discovery and tailoring communications to audiences of differing backgrounds. The paper claims that the framework explains inter-domain differences in adoption, and it offers recommendations for developers, organizations, and policymakers.

Significance. If its claims were fully supported, the paper would make a useful conceptual contribution to the human-AI interaction literature by shifting attention from reliance to the prior question of whether decision-makers adopt AI at all. The interview protocol, use-case taxonomy in the appendix, and the AI adoption sheet are transparent and potentially reusable, and the authors are explicit about several limitations. The cross-domain framing is valuable and goes beyond the mostly single-domain studies in prior work. However, the current evidence base is thin for the comparative claims: the legal cell has only two participants, several participants are researcher-insiders rather than practicing decision-makers, and the Figure 4 signs that carry the case-study analysis are not backed by a documented coding procedure. The contribution is therefore better described as a generative qualitative framework than as a confirmed empirical account of cross-domain adoption.

major comments (4)
  1. [Section 3.1, Table 1] The cross-domain claims rest on a small and partly second-hand sample. Table 1 lists only two legal participants (P05 and P06) and five of the sixteen participants (P01, P06, P10, P13, P14) are listed with primary area 'Research' rather than domain practice, which Section 3.1 acknowledges by including 'researcher-insiders' as a participant category. Because the central contribution is a comparative analysis across domains, the small cell sizes and the inclusion of proxy voices matter directly: a domain-level conclusion drawn from two interviews, one of whom is a researcher-insider, is not securely grounded. Section 6.3 acknowledges the limited sample, but the acknowledgment does not by itself protect the specific legal-domain contrasts in Section 5.2, which should at minimum be reframed as exploratory.
  2. [Section 3.3, Figure 4] The plus/minus/tilde signs in Figure 4 are the empirical payload of the case studies, but the manuscript reports no codebook, no unitizing or aggregation rule, and no inter-coder reliability check for converting open-ended interview content into these signs. Section 3.3 describes open coding and reflexive thematic analysis, which are appropriate for identifying themes, but it does not explain how individual mentions were combined into a single sign per factor per domain. Without a documented rule or agreement measure, the signs cannot be independently audited. The authors could either make the coding materials and aggregation procedure available or explicitly relabel the signs as interpretive summaries rather than coded evidence.
  3. [Sections 4 and 5] The four-factor framework and the case-study explanations are derived from the same 16 interviews, so the case studies are in-sample illustrations rather than independent tests of the framework. For example, the e-discovery analysis in Section 5.1 uses participant quotes from the same interview corpus that generated the factors in Section 4. The paper should avoid wording such as 'highlighting how our factors help explain inter-domain differences' (Section 1) if the explanation is not tested against new data; the external surveys cited in Sections 5.1 and 5.2 provide partial triangulation, but they are invoked selectively and do not validate the framework as a whole.
  4. [Section 5.2, Table 1] The claim that tailoring communications to audiences of differing backgrounds is 'absent in LEG' is an inference from non-mention in only two legal interviews (P05 and P06), both of whom are listed in Table 1. In open-ended interviews, absence of mention is weak evidence for absence in the domain, and the external survey [72] cannot fully substitute for targeted probing of the legal participants. The paper should either soften this to 'not mentioned by our legal participants' or provide a more systematic elicitation procedure (for example, asking participants about a fixed list of use cases) if the domain-level contrast is to be load-bearing.
minor comments (4)
  1. [Appendix A.2, Appendix B] Several typographical errors appear in the appendix, including 'Applicaiions', 'explinability', 'organizatioanl', 'Tehnology', 'availabtility', 'cpartners', and 'lieracy'; a careful proofreading pass is needed.
  2. [Section 6.1] Recommendation 3 contains 'Oftent' instead of 'Often', and the sentence beginning 'AI systems often demand substantial resources' would benefit from a period or semicolon for readability.
  3. [Tables 2-5] The use-case tables contain spelling errors such as 'reviwed', 'identifiy', 'pneumoonia', and 'radiolgists'; these should be corrected before publication.
  4. [Abstract and Section 4] The abstract's phrase 'the perceived implication of AI adoption' should be 'perceived implications' to match the factor name used in Section 4.4.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the four-factor framework is a thematic summary of the same interviews used for the illustrative case studies, but the paper makes no independent predictive claim and explicitly defers validation of the adoption sheet.

full rationale

The paper contains no formal derivation chain or quantitative fitting; its contribution is a qualitative thematic analysis of 16 semi-structured interviews. The four factors are described as identified from those interviews ("From these interviews, we identify key factors that shape decision-maker adoption"), and the two Section 5 case studies are explicitly framed as illustrations of the framework ("to illustrate how our AI adoption sheet helps systematically elucidate differences in decision-maker adoption"), not as out-of-sample tests. The adoption sheet is not claimed to be validated: the paper states "We leave the empirical assessment of the efficacy and usability of the proposed sheet as a critical direction for future work." Where cross-domain contrasts rest on small cells, such as the absence of tailoring communications in law, the authors triangulate with external sources (Warren et al. [72]; PWC [51]), so the claims are not solely self-referential. Self-citations such as [19], [34], and [62] are ordinary supporting references and carry no load-bearing uniqueness or derivation argument. The absence of inter-coder reliability and the small legal sample are validity/generalizability concerns acknowledged in Section 6.3, but they do not make any result equivalent to its input by construction. No circular step meeting the required evidentiary standard is present.

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

The central claim rests on the representativeness of the interview sample and the reliability of the qualitative coding. No mathematical parameters or invented entities are involved. The AI adoption sheet is a proposed practical tool, not an empirical entity.

assumptions (3)
  • domain assumption The four selected domains are representative of AI decision-making contexts
    The paper selects journalism, law, medicine, and the public sector to span predictive and generative applications (Section 3.1).
  • domain assumption Participants with prior AI experience can report on the full range of barriers to adoption, including reasons for non-adoption
    Recruitment required familiarity with AI tools, which may exclude non-adopters (Section 3.1, limitations in Section 6.3).
  • domain assumption Reflexive thematic analysis by the authors reliably captures the factors without significant researcher bias
    The analysis approach is described in Section 3.3 but no codebook, inter-rater reliability, or member checking is reported.

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Cite this review

Pith. "Pith review of Why Do Decision Makers (Not) Use AI? A Cross-Domain Analysis of Factors Impacting AI Adoption." pith.science (2026). https://pith.science/paper/ZNHUNGNK

@misc{pith2026250800723,
  author       = {Pith},
  title        = {Pith review of: Why Do Decision Makers (Not) Use AI? A Cross-Domain Analysis of Factors Impacting AI Adoption},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZNHUNGNK}},
  note         = {Machine review of arXiv:2508.00723}
}
read the original abstract

Growing excitement around deploying AI across various domains calls for a careful assessment of how human decision-makers interact with AI-powered systems. In particular, it is essential to understand when decision-makers voluntarily choose to consult AI tools, which we term decision-maker adoption. We interviewed experts across four domains -- medicine, law, journalism, and the public sector -- to explore current AI use cases and perceptions of adoption. From these interviews, we identify key factors that shape decision-maker adoption of AI tools: the decision-maker's background, perceptions of the AI, consequences for the decision-maker, and perceived implications for other stakeholders. We translate these factors into an AI adoption sheet to analyze how decision-makers approach adoption choices through comparative, cross-domain case studies, highlighting how our factors help explain inter-domain differences in adoption. Our findings offer practical guidance for supporting the responsible and context-aware deployment of AI by better accounting for the decision-maker's perspective.

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.