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REVIEW 3 major objections 4 minor 17 references

Trusting AI to increase productivity? Perspectives Across the Global North and South

T0 review · 3 major / 4 minor · reviewed 2026-07-14 · grok-4.5

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Pith's one-line read Higher trust in GenAI does not by itself produce stronger productivity gains; access, task type, and verification load also decide outcomes.

desk verdict Useful literature-gap map and honest exploratory survey, but the headline trust–productivity contrast rests on n=3 and should not be treated as a stable finding yet. read the letter →

arxiv 2607.10488 v1 pith:WRJX4N4L submitted 2026-07-11 cs.SE

Adam Bokun , Shalini Chakraborty This is my paper · ORCID
classification cs.SE
keywords TrustGenAIProductivityGlobalSouthsoftwareengineeringperceivedaccessbarriersverification
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 exploratory paper asks whether trust in generative AI is enough to turn tools into real productivity for academics and software developers, especially under Global South conditions. A systematic search found no peer-reviewed study that jointly treated GenAI trust, productivity, and Global South settings; grey literature mainly offered potential-gain estimates and noted uneven readiness. A small ongoing survey (36 valid responses) then compared people by birth and work region. Respondents born and working in the Global South reported the highest average trust but only modest productivity scores and roughly 11 minutes saved per task, while those born and working outside the Global South reported lower trust yet higher productivity scores and about 21 minutes saved. The paper therefore argues that calibrated trust, reliable access, and the cost of checking outputs jointly determine whether GenAI actually helps.

What carries the argument

Three-way geographic grouping (Global-South-born + Global-South-working, mixed, non-Global-South) combined with average Likert scores for trust (six items) and perceived productivity (seven items), plus self-reported minutes saved and open-ended themes about verification and barriers.

What would settle it

A larger survey or field study that keeps the same trust and productivity items, balances the Global-South-born-and-working cell, and still finds either that higher trust reliably predicts higher productivity across regions or that the reverse pattern disappears once access and verification load are controlled.

Watch

Extended reading notes

Core claim

Respondents born and working in the Global South averaged higher trust in GenAI (0.83) than respondents born and working outside it (0.30), yet did not report stronger productivity gains or time savings; the non-Global-South group averaged higher productivity (0.68) and roughly twice the estimated minutes saved per task. Trust alone is therefore insufficient; access, task type, and verification effort also shape outcomes.

Load-bearing premise

That averages from a survey of only 36 people, including just three who were both born and working in the Global South, can still usefully describe cross-regional differences in trust and productivity.

Editorial extensions

If this is right

  • Productivity research on GenAI must measure access costs, institutional permissions, and verification time alongside trust, not treat trust as a sufficient cause.
  • Global-South-focused studies cannot assume that higher reported trust will translate into larger time savings under current tool and infrastructure conditions.
  • Workplace and platform design that reduces the need for constant output checking may convert existing trust into actual productivity gains more effectively than trust-building campaigns alone.
  • Comparative samples that include people who have moved between regions can surface transitional access and training effects that pure North/South binaries miss.

Reading between the lines

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

  • If verification load is the hidden bottleneck, free or low-capability models may systematically under-deliver productivity even among high-trust users, widening rather than closing regional gaps.
  • The same pattern may appear in other high-stakes knowledge work (medicine, law, education) where fluent but unchecked AI output creates rework.
  • Policy that only subsidizes access without also funding training in critical evaluation of AI output may raise trust scores without raising net productivity.
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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. The paper reports an exploratory multi-method study of trust in GenAI and perceived productivity among academics and software developers, motivated by Global South contexts. A systematic literature review (Google Scholar 2022–2025, staged screening to CORE A/B venues) found zero peer-reviewed papers at the intersection of GenAI trust, productivity, and Global South settings; grey literature (19 screened sources from McKinsey, OECD, EY India, etc.) offered only limited, mostly potential-gain estimates. An ongoing survey (n=36 valid responses) groups respondents by birth and work region, computes mean trust (6 adapted Likert items) and productivity (7 items) scores coded −2 to 2, and reports estimated time savings. Preliminary descriptive results indicate higher average trust among the three respondents born and working in the Global South (0.83) than among non-Global-South respondents (0.30), yet lower or comparable productivity scores and smaller time savings (~11 min vs ~21 min), leading the authors to conclude that trust alone is insufficient and that access, task type, and verification load also matter.

Significance. If the trust–productivity decoupling holds under better-powered sampling, the work would usefully document that calibrated trust and infrastructural conditions jointly shape GenAI productivity gains—an under-studied intersection for empirical software engineering. Strengths include a transparent SLR pipeline (Table 1), explicit grey-literature inclusion rules, dual-author checking of quantitative aggregates and open-response codes, adaptation of published trust and productivity instruments, and public release of anonymized data, instrument, and coding scheme on Zenodo. These practices raise the bar for reproducibility of early-stage survey work. The contribution remains modest and provisional because the headline cross-regional contrast rests on an extremely small pure-Global-South cell; the paper’s main value is therefore the documented literature gap and the open instrument rather than a stable empirical finding.

major comments (3)
  1. §4.1–4.2, Table 2 and Figure 1: The central claim (Abstract, Discussion, Conclusion) that higher trust among Global-South-born-and-working respondents (mean trust 0.83) does not translate into stronger productivity gains or time savings relative to non-Global-South respondents rests almost entirely on the n=3 cell. With three observations a single atypical respondent can reverse both rankings; the reported averages are therefore unstable descriptive signals. The authors correctly note that the cell is “too small for robust inference,” yet still present the directional contrast as the headline emerging result. Either enlarge the pure-GS cell substantially or reframe the paper as a pure methods/gap paper that does not advance any cross-regional ranking.
  2. §3.1 and Table 1: The SLR search string forces the conjunction of “software engineering” with Global-South terms and then aggressively filters to CORE A/B conference proceedings only, yielding zero papers. While the transparency of the pipeline is commendable, the claim of “no peer-reviewed evidence at the intersection” is sensitive to these design choices; journals, workshops, and non-SE venues that discuss GenAI trust and productivity in low-resource settings are systematically excluded. A sensitivity check that relaxes the venue or SE constraints (or reports the 38 full-text papers that were screened out) is needed before the gap claim can be treated as load-bearing.
  3. §3.4 and §4.2: Aggregate trust and productivity scores are simple unweighted means of six and seven Likert items with no reported internal consistency (Cronbach’s α or equivalent), item-total correlations, or factor structure. Because the subsequent geographic contrasts and the “trust is not enough” interpretation rest on these composites, basic scale diagnostics are required to establish that the averages are measuring coherent constructs rather than noise.
minor comments (4)
  1. §4.2, time-savings coding: Mapping “more than 30 minutes” to exactly 30 minutes is acknowledged as conservative, but the resulting group means (Table 3) should be accompanied by the raw category frequencies so readers can judge sensitivity to the upper-bound choice.
  2. Figure 1 and Figure 2 are described but not rendered in the supplied manuscript text; ensure axis labels, error bars (or explicit statement of their absence), and sample sizes per bar are visible in the camera-ready version.
  3. §2: The definition of “calibrated trust” is useful; a brief forward reference to how the survey items operationalize (or fail to operationalize) calibration would tighten the link between background and measures.
  4. References: Several grey-literature URLs lack stable archival identifiers; consider adding DOIs or Wayback Machine snapshots for long-term citability.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: survey averages and literature-gap claims are independent descriptive results, not self-definitional or fitted predictions.

full rationale

This is an exploratory empirical paper (SLR + grey literature + n=36 survey), not a first-principles or model-fitting derivation. Trust scores are simple averages of six adapted Likert items; productivity scores are averages of seven separate items; geographic groups are defined by self-reported birth and work region. None of these constructs is defined in terms of the others, so the reported contrast (higher mean trust in the GS-born+GS-working cell without correspondingly higher productivity/time savings) is not forced by construction. The literature-gap claim is an empirical screening outcome (4,977 → 0 papers meeting inclusion criteria), not a restatement of a prior definition. Self-citations (e.g., Baltes et al. with overlapping authorship) appear only as background on trust in AI assistants and are not load-bearing for the survey findings or the gap claim. There are no fitted parameters renamed as predictions, no uniqueness theorems imported from the authors, and no ansatz smuggled via citation. Circularity burden is zero; any weakness is statistical (tiny n=3 cell), not circular.

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

The paper is empirical and exploratory; it does not introduce free physical/math parameters or new theoretical entities. Load-bearing background commitments are domain definitions (Global South as analytical category, perceived productivity via adapted Likert items, trust as willingness to rely under uncertainty) and the operational grouping of respondents by self-reported birth and work region. No invented mediators or fitted constants drive the central contrast.

assumptions (4)
  • domain assumption Global South is a useful analytical category for unequal access to economic, technological, and institutional resources even though it is not homogeneous.
    Stated in Background and used to define respondent groups and motivate the study; without it the North/South contrast collapses.
  • domain assumption Perceived productivity (time savings, reduced effort, quality, satisfaction) measured by averaged Likert items is a valid proxy for GenAI-related productivity outcomes in this exploratory setting.
    Section 2 and Survey Design; items adapted from prior work [16] and treated as the productivity construct throughout Results.
  • domain assumption Trust in GenAI can be measured by averaging six adapted Likert items from a prior student GenAI-trust instrument [1].
    Survey Design §3.3; the aggregate trust score is the left-hand side of the central contrast.
  • ad hoc to paper Self-reported birth region and current working region sufficiently capture the infrastructural and institutional conditions that may moderate trust–productivity links.
    Operational grouping in Data Analysis and Table 2; the three-cell design is the paper's own analytic frame rather than a standard validated typology.

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Cite this review

Pith. "Pith review of Trusting AI to increase productivity? Perspectives Across the Global North and South." pith.science (2026). https://pith.science/paper/WRJX4N4L

@misc{pith2026260710488,
  author       = {Pith},
  title        = {Pith review of: Trusting AI to increase productivity? Perspectives Across the Global North and South},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WRJX4N4L}},
  note         = {Machine review of arXiv:2607.10488}
}
read the original abstract

Generative AI (GenAI) tools are widely used in academia and software development, where productivity gains may depend not only on technical capabilities but also on users' trust and contextual factors. This paper presents emerging results from an exploratory study investigating the relationship between trust in GenAI and perceived productivity, motivated by Global South contexts. We conducted a systematic literature review, complemented by a grey literature analysis and a survey study. The literature review identified no peer-reviewed evidence at the intersection of GenAI trust, productivity, and Global South settings, while the grey literature revealed only limited insights. At the time of writing, the survey has received 36 valid responses from participants across both the Global North and Global South, including individuals with cross-regional experiences. Preliminary results suggest that respondents born and working in the Global South tended to trust AI more, but did not usually report clear productivity gains from using it. In contrast, respondents born and working outside the Global South reported stronger productivity gains and greater time savings, even though they showed less trust in generative AI. These findings suggest that trusting AI is not enough on its own; productivity also depends on access, the type of task, and how much users need to check the output.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

17 extracted references · 1 canonical work pages

  1. [1]

    Trust in generative ai among students: An exploratory study

    1 Matin Amoozadeh, David Daniels, Daye Nam, Aayush Kumar, Stella Chen, Michael Hilton, Sruti Srinivasa Ragavan, and Mohammad Amin Alipour. Trust in generative ai among students: An exploratory study. InProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1, SIGCSE 2024, page 67–73, New York, NY, USA,

    2024

  2. [2]

    2 Sebastian Baltes, Timo Speith, Brenda Chiteri, Seyedmoein Mohsenimofidi, Shalini Chakraborty, and Daniel Buschek

    Association for Computing Machinery.doi:10.1145/3626252.3630842. 2 Sebastian Baltes, Timo Speith, Brenda Chiteri, Seyedmoein Mohsenimofidi, Shalini Chakraborty, and Daniel Buschek. On the need to rethink trust in ai assistants for software development: A critical review.IEEE Transactions on Software Engineering, 52:1265–1281,

    doi:10.1145/3626252.3630842

  3. [3]

    5Nour Dados and Raewyn Connell

    doi:10.1109/ICSE55347.2025.00087. 5Nour Dados and Raewyn Connell. The global south.Contexts, 11(1):12–13,

    doi:10.1109/icse55347.2025.00087 2025

  4. [4]

    https://www.ey.com/en_in/insights/ai/generative-ai-india-2025-report,

    2025

  5. [5]

    Ai at work.https://www.globalization-partners.com/resources/ 2025-ai-at-work-report/,

    7 Globalization Partners. Ai at work.https://www.globalization-partners.com/resources/ 2025-ai-at-work-report/,

    2025

  6. [6]

    10 Elisavet Koutsiana, Johanna Walker, Michelle Nwachukwu, Bohui Zhang, Albert Meroño- Peñuela, and Elena Simperl

    URL:https://www.sciencedirect.com/science/article/pii/ S2666990024000120,doi:10.1016/j.cmpbup.2024.100145. 10 Elisavet Koutsiana, Johanna Walker, Michelle Nwachukwu, Bohui Zhang, Albert Meroño- Peñuela, and Elena Simperl. Knowledge prompting: How knowledge engineers use generative ai.Journal of Web Semantics, 88:100873,

    doi:10.1016/j.cmpbup.2024.100145 2024

  7. [7]

    11 John D

    URL: https://www.sciencedirect.com/ science/article/pii/S1570826825000149,doi:10.1016/j.websem.2025.100873. 11 John D. Lee and Katrina A. See. Trust in automation: designing for appropriate reliance. Human factors, 46(1):50–80,

    work page doi:10.1016/j.websem.2025.100873 2025

  8. [8]

    URL:https://journals.sagepub.com/ doi/abs/10.1518/hfes.46.1.50_30392,doi:10.1518/hfes.46.1.50\_30392

    PMID: 15151155. URL:https://journals.sagepub.com/ doi/abs/10.1518/hfes.46.1.50_30392,doi:10.1518/hfes.46.1.50\_30392. 12 McKinsey & Company. The economic potential of generative ai: The next productiv- ity frontier. https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/ the-economic-potential-of-generative-ai-the-next-productivity-frontier ,

    doi:10.1518/hfes.46.1.50_30392

Show all 17 references
  1. [9]

    The great acceleration: Cio perspectives on generative ai

    13 MIT Technology Review Insights. The great acceleration: Cio perspectives on generative ai. https://elements.visualcapitalist.com/wp-content/uploads/2024/08/ 1723378482844.pdf,

    2024

  2. [10]

    17 Han Qiao, Jo Vermeulen, George Fitzmaurice, and Justin Matejka

    Association for Computing Machinery.doi:10.1145/3640543.3645198. 17 Han Qiao, Jo Vermeulen, George Fitzmaurice, and Justin Matejka. To use or not to use: Impatience and overreliance when using generative ai productivity support tools. InProceedings of the 2025 CHI Conference o...

    2025 doi

  3. [11]

    18 Massimo Ragnedda and Anna Gladkova

    Association for Computing Machinery.doi:10.1145/3706598.3714103. 18 Massimo Ragnedda and Anna Gladkova. Understanding digital inequalities in the global south. InDigital inequalities in the global south, pages 17–30. Springer,

    doi

  4. [12]

    Gemini at work: Knowledge workers’ perceptions and assessment of productivity gains

    19 Na Sun and Donald Kalar. Gemini at work: Knowledge workers’ perceptions and assessment of productivity gains. InProceedings of the 2025 ACM Designing Interactive Systems Conference, DIS ’25, page 3681–3695, New York, NY, USA,

    2025

  5. [13]

    doi:10.1145/3715336.3735679

    Association for Computing Machinery. doi:10.1145/3715336.3735679. 20 Maxim Tabachnyk, Xu Shu, Alexander Frömmgen, Pavel Sychev, Vahid Meimand, Ilia Krets, Stanislav Pyatykh, Abner Araujo, Kristóf Molnár, and Satish Chandra. Achieving productivity gains with ai-based ide featur...

    doi

  6. [14]

    22 Takane Ueno, Yuto Sawa, Yeongdae Kim, Jacqueline Urakami, Hiroki Oura, and Katie Seaborn

    doi:10.1007/s10664-026-10848-w. 22 Takane Ueno, Yuto Sawa, Yeongdae Kim, Jacqueline Urakami, Hiroki Oura, and Katie Seaborn. Trust in human-ai interaction: Scoping out models, measures, and methods. InExtended Abstracts of the 2022 CHI Conference on Human Factors in Computing ...

    2022 doi

  7. [15]

    Association for Computing Machinery.doi:10.1145/3491101. 3519772. 23 Michael Vössing, Niklas Kühl, Matteo Lind, and Gerhard Satzger. Designing transparency for effective human-ai collaboration.Information Systems Frontiers, 24(3):877–895,

    doi

  8. [16]

    doi:10.1145/3706599.3706670

    Association for Computing Machinery. doi:10.1145/3706599.3706670. 25 Ilya Zakharov, Ekaterina Koshchenko, and Agnia Sergeyuk. Ai in software engineering: Perceived roles and their impact on adoption. InProceedings of the 33rd ACM International Conference on the Foundations of ...

    doi

  9. [17]

    Association for Computing Machinery.doi:10.1145/3696630. 3730563

    doi

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