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REVIEW 4 major objections 4 minor 60 references

Diversity and Inclusion in AI: Insights from a Survey of AI/ML Practitioners

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

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Pith's one-line read This paper tries to establish that AI practitioners' belief in diversity and inclusion is not matched by their organisations' practices.

desk verdict Small, transparent practitioner survey; the perception-practice gap claim holds, but the subgroup barrier claims need statistical tempering. read the letter →

arxiv 2505.18523 v1 pith:XU7WQC5W submitted 2025-05-24 cs.CY

Sidra Malik , Muneera Bano , Didar Zowghi This is my paper
classification cs.CY
keywords DiversityandInclusionAIethicsAI/MLpractitionerssurveylifecyclebiasmitigationgovernancefivepillarsofecosystem
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 paper tries to establish that AI and machine-learning practitioners broadly believe diversity and inclusion (D&I) make AI fairer and more trustworthy, but their organisations implement these principles only partially and unevenly. Using a survey of 61 industry professionals, it shows that most respondents credit diverse teams and datasets with reducing bias, while practices such as bias audits, inclusive governance, and post-deployment monitoring lag far behind. The authors identify under-representation of marginalised groups, lack of organisational transparency, and limited awareness among early-career professionals as the main barriers. If the finding is right, the gap between ethical commitment and everyday AI development is a structural feature of current practice rather than a lack of goodwill.

What carries the argument

The organising device is the five-pillar model of the AI ecosystem — humans, data, system, process, and governance — used to structure the survey and its analysis. Each of the three research questions (perceived impact, current practices, challenges) is asked across those pillars, so the results show where in the lifecycle D&I is strongest and where it drops away. The second mechanism is the mixed-methods analysis: descriptive statistics and cross-tabulations identify demographic patterns, while thematic coding of open-ended responses captures the reasoning behind the numbers.

What would settle it

A representative survey or audit of AI/ML organisations that finds most conduct regular bias audits and apply D&I principles after deployment would falsify the paper's central claim of a systematic perception-practice disconnect.

Watch

Extended reading notes

Core claim

The paper's central claim is that there is a systematic disconnect between what AI/ML practitioners believe about D&I and what their organisations actually do. For example, 87% of respondents say diverse and inclusive data reduces biased or discriminatory outcomes, but only 41% report that their organisations perform bias audits; 67% believe diverse teams reduce bias, yet only about half report active recruitment practices. D&I principles are incorporated at the pre-development and development stages by 59% of respondents but at the post-development stage by just 41%. Only 18% say their organisations have fully implemented D&I governance policies, and 20% do not know whether such policies exist. The authors read these patterns as evidence that D&I is treated as an early-stage or compliance matter rather than embedded across the AI lifecycle, and they link the gap to the under-representation of marginalised groups, weak transparency, and uneven awareness among early-career staff.

Load-bearing premise

The survey treats its self-selected, network-recruited sample of 61 respondents — 70% Asian, 67% male, 52% with 1–5 years' experience — as sufficient evidence for comparisons across gender, ethnicity, experience, and organisation size.

Editorial extensions

If this is right

  • Organisations that already have D&I policies should expect a drop-off after the development phase and should target monitoring, feedback, and audit processes specifically.
  • Because data anonymisation is the most commonly cited data-diversity method yet can hide the demographic detail needed for bias detection, fairness work needs privacy-preserving ways to retain that detail.
  • Early-career professionals' uncertainty about their organisations' D&I efforts points to a communication gap that more visible governance and training could close.
  • Regulated industries such as banking, finance, and healthcare may need tailored guidance, since compliance pressures can crowd out proactive fairness measures.
  • Fully implemented D&I governance is rare in the sample, so enforceable standards and transparency requirements are likely needed to make D&I routine rather than aspirational.

Reading between the lines

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

  • We infer that the perceived-benefit percentages are probably upper bounds, because people who volunteer for a D&I survey are likely to care more about D&I than the average practitioner; a representative sample might show less consensus and an even wider practice gap.
  • We infer that the sceptical minority (10%, mostly male developers, often in small organisations) is a target population for developer-facing education, since policy-level documents seem not to reach them.
  • The five-pillar structure implies a testable prediction: organisations with strong early-stage D&I but weak post-development oversight will show more bias drift in deployed systems, so linking survey answers to audit outcomes could validate the disconnect.
  • The 2024–2025 rollback of corporate DEI programs described in the paper would be expected to widen this perception-practice gap; repeating the same survey over time could measure that effect.
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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. The paper reports a survey-based study of 61 AI/ML practitioners recruited through convenience and network sampling, examining how diversity and inclusion (D&I) principles are perceived, implemented, and challenged across the AI lifecycle. The survey is organized around five pillars (humans, data, process, system, governance) and collects both multiple-choice and open-ended responses. The central descriptive finding is a disconnect between practitioners' widespread agreement on the value of D&I (e.g., 67% say diverse teams reduce bias) and actual organizational practices (e.g., only 41% report bias audits, and D&I consideration declines from 59% in pre-development/development to 41% in post-development). The authors also identify three major barriers: under-representation of marginalized groups, lack of organizational transparency, and limited awareness among early-career professionals. The paper concludes by discussing implications for researchers, practitioners, and policymakers and acknowledges external validity limitations in Section 7.2.

Significance. If the results are credited, the paper contributes useful empirical evidence on a perception-practice gap in D&I within AI organizations, an area where prior work has been largely theoretical. The manuscript's strengths include a pilot-tested instrument, a mixed-methods design with open-ended questions, and an unusually candid threats-to-validity section that acknowledges self-selection bias and demographic imbalances. However, the small, self-selected sample (n=61; 70% Asian, 67% male, 52% early-career) severely limits generalizability, and the paper's more granular comparative claims about demographic subgroups and about 'limited awareness among early-career professionals' are not statistically supported. Because these specific claims appear in the abstract as major barriers, they are load-bearing for the paper's headline message. With appropriate qualification of those claims, the descriptive core of the paper remains a useful snapshot of practitioner perspectives.

major comments (4)
  1. [§5.2, §5.3, and Abstract] The abstract's claim that 'limited awareness among early-career professionals' is a major barrier rests on subgroup analyses with very small denominators and no uncertainty quantification. For example, the 10% of respondents who believe diverse teams have minimal or no impact is about 6 of 61 respondents (Fig. 2, §5.2), yet this group is cross-tabulated by gender, ethnicity, age, and organization size, yielding single-digit cell counts. Similarly, §5.3 says 'uncertainty about D&I initiatives is more common among early-career professionals (75%)'; this is a conditional proportion of the 'Not Sure' group and does not demonstrate that early-career respondents are more likely to be unsure than more experienced respondents. With n=61, such differences could easily arise by chance. Please either report confidence intervals or other measures of uncertainty and explicitly temper these comparative claims, or remove them from the abstract.
  2. [§6.1 and §7.3] The paper equates 'Not Sure' responses with limited awareness, as in §6.1: 'Many early-career professionals also reported uncertainty about their organisation's D&I in AI efforts, pointing to possible communication and visibility gaps.' The survey instrument (Appendix A) contains no direct measure of awareness, and 'Not Sure' could equally reflect question ambiguity, lack of direct knowledge due to role specialization (as the authors themselves note for data governance teams), or genuine uncertainty. Section 7.3 discusses construct validity but does not address this specific conflation. Please justify the interpretation of 'Not Sure' as awareness, or soften the wording to avoid overreach.
  3. [§6.3 and Fig. 7] The governance statistics are internally inconsistent across sections. Section 5.3 and Fig. 7 report 51% 'somewhat established', 18% 'fully implemented', 20% 'I don't know', and 13% 'None', which implies 69% of respondents report some form of policy. Section 6.3, however, states that '55% of respondents reported some presence of structured D&I governance policies'—a figure that is not presented in Section 5 or in Fig. 7. In addition, the sentence 'Among respondents who do not know, 13% of the respondents confirm that no such policies exist' is self-contradictory, since respondents who do not know cannot confirm the absence of policies. Please reconcile these numbers and clarify the exact denominator and response options used.
  4. [§6.2] The claim that 'only 26% of women in our survey citing barrier to career progression, compared to 70% of men' is not traceable to any item in the survey instrument in Appendix A; none of the Q17 response options refers to a 'barrier to career progression'. The same subsection states that '70% of early-career professionals (1–5 years of experience) believe that diverse teams improve AI fairness,' but the supporting cross-tabulation is not shown in the results. Please provide the underlying data or remove these unsupported comparative statements.
minor comments (4)
  1. [Throughout] The manuscript contains several typographical errors that should be corrected, for example 'citepd' (§5.3), 'demograhics' (§5.3), 'hey' (§5.3), 'lifescyle' (§5.3), and 'om D&I' in the caption of Fig. 2.
  2. [§5.4 and Appendix B] The text refers to 'Figure 9 in Appendix' when discussing challenges in creating inclusive AI systems, but Figure 9 appears in the main body of the paper; the additional figures in Appendix B are numbered 10–16. Please correct the cross-reference.
  3. [Appendix A] The survey instrument is not numbered consecutively: the 'Systems' question appears as 'Q: What challenges are faced...' without a question number, so the subsequent governance question is numbered Q21 but no Q20 exists. Please renumber the questions consistently.
  4. [Abstract] The abstract writes 'Machine Learning(ML)' without a space before the parenthesis; please fix the spacing for consistency with journal style.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper reports direct survey observations; the self-cited five-pillar framework is an organizing lens, not a derivation that forces the findings.

full rationale

This paper is an empirical survey study rather than a derivation, and I found no step in which a claimed result reduces to an input by construction or via load-bearing self-citation. The central claim, that practitioners recognize the value of D&I while implementation remains inconsistent, is presented as a descriptive summary of the 61 survey responses and is not derived from any fitted parameter or prior theorem. The five-pillar framework is cited from the authors' own prior work (Zowghi and da Rimini, 2023) and used to structure the survey and organize the discussion, but the framework does not determine the reported percentages, barriers, or cross-tabulations; those quantities come from the respondents' self-reports. The related self-citations (e.g., Bano et al., 2024a; Shams et al., 2023; Zowghi and Bano, 2024) are used as supporting literature or as prior systematic review, not as the sole justification for the paper's empirical conclusions. No uniqueness theorem is invoked, no ansatz is smuggled in through citation, and no known result is merely renamed as a new contribution. The acknowledged limitations in Section 7, including the small self-selected sample and limited statistical generalizability, are threats to validity and precision rather than circularity. Concerns about small cell counts and absence of inferential tests are legitimate correctness risks, but they do not show that the analysis assumes what it claims to find. The paper's findings are self-contained in the sense that they are summaries of the collected data, so the appropriate circularity score is 0.

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

This is an empirical survey with no fitted numeric parameters. The main load-bearing assumptions are the validity of the five-pillar framework, the accuracy of self-reported data, and the sufficiency of the sample for cross-demographic analysis. The paper introduces no new entities, forces, or mediators.

assumptions (3)
  • domain assumption The five-pillar model of the AI ecosystem (humans, data, process, system, governance) is a valid and comprehensive framework for analyzing D&I in AI.
    The entire survey design and analysis is structured around this model from Zowghi and da Rimini (2023), cited in Section 1 and Section 2. If this framework is not appropriate, the mapping of questions to pillars may not capture D&I in practice.
  • domain assumption Self-reported survey responses accurately reflect actual organizational practices and the respondents' genuine views.
    The study relies on participants' perception of their organization's D&I efforts; social desirability bias and limited awareness could distort these reports. Discussed in Section 7.1 (Internal Validity).
  • domain assumption The self-selected sample is sufficient to produce meaningful cross-demographic comparisons.
    The paper conducts cross-tabulations by gender, experience, organization size, and ethnicity despite a small and skewed sample. Citing Section 7.2, the authors acknowledge limited generalizability but still present these comparisons as findings.

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Cite this review

Pith. "Pith review of Diversity and Inclusion in AI: Insights from a Survey of AI/ML Practitioners." pith.science (2026). https://pith.science/paper/XU7WQC5W

@misc{pith2026250518523,
  author       = {Pith},
  title        = {Pith review of: Diversity and Inclusion in AI: Insights from a Survey of AI/ML Practitioners},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XU7WQC5W}},
  note         = {Machine review of arXiv:2505.18523}
}
read the original abstract

Growing awareness of social biases and inequalities embedded in Artificial Intelligence (AI) systems has brought increased attention to the integration of Diversity and Inclusion (D&I) principles throughout the AI lifecycle. Despite the rise of ethical AI guidelines, there is limited empirical evidence on how D&I is applied in real-world settings. This study explores how AI and Machine Learning(ML) practitioners perceive and implement D&I principles and identifies organisational challenges that hinder their effective adoption. Using a mixed-methods approach, we surveyed industry professionals, collecting both quantitative and qualitative data on current practices, perceived impacts, and challenges related to D&I in AI. While most respondents recognise D&I as essential for mitigating bias and enhancing fairness, practical implementation remains inconsistent. Our analysis revealed a disconnect between perceived benefits and current practices, with major barriers including the under-representation of marginalised groups, lack of organisational transparency, and limited awareness among early-career professionals. Despite these barriers, respondents widely agree that diverse teams contribute to ethical, trustworthy, and innovative AI systems. By underpinning the key pain points and areas requiring improvement, this study highlights the need to bridge the gap between D&I principles and real-world AI development practices.

Figures

Figures reproduced from arXiv: 2505.18523 by the authors.

Figure 1
Figure 1. Gender vs. Experience Reduces bias in AI outcomes, 67% Enhances creativity and innovation in AI development, 57% Leads to more comprehensive problem-solving approaches, 59% Strengthens user trust and acceptance of the AI system, 51% Has minimal or no impact on the ethical outcomes of the AI system, 10% Not sure, 10% [PITH_FULL_IMAGE:figures/full_fig_p010_1.png] view at source ↗
Figure 2
Figure 2. Perceived Impact of Diverse and Inclusive Teams om D&I in AI [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Perceived Impact: Effect of D&I on processes and systems [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figures from the paper (13 more)
Figure 4
Figure 4. Figure 4: Perceived Impact:Effect of government policies on success of D&I in AI initia [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]
Figure 5
Figure 5. Figure 5: Current Practices: Ensuring diverse and inclusive datasets [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: Current Practices: Bias mitigation strategies in datasets [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 7
Figure 7. Figure 7: Current Practices: Government Structures supporting D&I [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]
Figure 8
Figure 8. Figure 8: Challenges in diverse and inclusive workforce [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]
Figure 9
Figure 9. Figure 9: Challenges in establishing D&I governance policies [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]
Figure 10
Figure 10. Figure 10: Demographics (a) Organisation Size (b) Experience (c) Roles 0% 10% 20% 30% 40% 50% 1-50 51-200 201-1000 More than 1000 employees 0% 5% 10% 15% 20% 25% 30% 35% 1-5 years 6-10 years Less than 1 year More than 10 years 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% Development M…
Figure 11
Figure 11. Figure 11: Gender vs. Roles [PITH_FULL_IMAGE:figures/full_fig_p035_11.png]
Figure 12
Figure 12. Figure 12: Gender vs. Ethnicity 0% 10% 20% 30% 40% 50% 60% Development Management Governance Researcher AI Instructor Sales Co-founder Ethicist Trainee Women Men Prefer not to say 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% Asian Caucasian Middle Eastern or North African Southern …
Figure 13
Figure 13. Figure 13: Perceived Impact of Diverse and Inclusive Data [PITH_FULL_IMAGE:figures/full_fig_p036_13.png]
Figure 14
Figure 14. Figure 14: Current Practices: Ensuring diverse and inclusive teams [PITH_FULL_IMAGE:figures/full_fig_p036_14.png]
Figure 15
Figure 15. Figure 15: Current Practices: D&I principles in AI lifecycle [PITH_FULL_IMAGE:figures/full_fig_p037_15.png]
Figure 16
Figure 16. Figure 16: Challenges in creating diverse and inclusive AI systems [PITH_FULL_IMAGE:figures/full_fig_p037_16.png]

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    2024

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    Zowghi, D. and da Rimini, F. (2023). Diversity and Inclusion in AI . In Lu, Q., Zhu, L., Whittle, J., and Xu, X., editors, Responsible AI: Best Practices for Creating Trustworthy AI Systems , chapter 11, pages 213--230. Addison-Wesley Professional, 1 edition

    2023

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    write newline

    " write newline " cite write " FUNCTION editor.postfix editor num.names #1 > "( )" "( )" if FUNCTION editor.trans.postfix editor num.names #1 > "( )" "( )" if FUNCTION trans.postfix translator num.names #1 > "( )" "( )" if FUNCTION authors.editors.reflist.apa5 'field := 'dot :...

  41. [49]

    sn-aps.bst

    FUNCTION identify.aps.version "sn-aps.bst" " [2024/07/19 v1.1 APS bibliography style]" * top ENTRY address author booktitle chapter doi edition editor eid howpublished institution journal key keywords month note number organization pages publisher school series title type url ...

    2024

  42. [50]

    write newline

    " write newline "" before.all 'output.state := FUNCTION if.digit duplicate "0" = swap duplicate "1" = swap duplicate "2" = swap duplicate "3" = swap duplicate "4" = swap duplicate "5" = swap duplicate "6" = swap duplicate "7" = swap duplicate "8" = swap "9" = or or or or or or...

  43. [51]

    sn-basic.bst

    FUNCTION identify.basic.version "sn-basic.bst" " [2024/07/19 v1.1 bibliography style]" * top ENTRY address archive author booktitle chapter doi edition editor eid eprint howpublished institution journal key keywords month note number organization pages publisher school series ...

    2024

  44. [52]

    write newline

    " write newline "" before.all 'output.state := FUNCTION add.period duplicate empty 'skip "." * add.blank if FUNCTION if.digit duplicate "0" = swap duplicate "1" = swap duplicate "2" = swap duplicate "3" = swap duplicate "4" = swap duplicate "5" = swap duplicate "6" = swap dupl...

  45. [53]

    write newline

    " write newline "" before.all 'output.state := FUNCTION output.doi doi empty skip "doi:" doi * "" * output if FUNCTION format.archive archivePrefix empty "" archivePrefix ":" * if FUNCTION format.primaryClass primaryClass empty "" " [" primaryClass * "] " * if FUNCTION format....

  46. [54]

    write newline

    " write newline "" before.all 'output.state := FUNCTION string.to.integer 't := t text.length 'k := #1 'char.num := t char.num #1 substring 's := s is.num s "." = or char.num k = not and char.num #1 + 'char.num := while char.num #1 - 'char.num := t #1 char.num substring FUNCTI...

  47. [55]

    write newline

    " write newline "" before.all 'output.state := FUNCTION string.to.integer 't := t text.length 'k := #1 'char.num := t char.num #1 substring 's := s is.num s "." = or char.num k = not and char.num #1 + 'char.num := while char.num #1 - 'char.num := t #1 char.num substring FUNCTI...

  48. [56]

    sn-nature.bst

    FUNCTION identify.nature.version "sn-nature.bst" " [2024/07/19 v1.1 bibliography style]" * top ENTRY address archive author booktitle chapter edition editor eprint howpublished institution journal key keywords month note number organization pages publisher school series title ...

    2024

  49. [57]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

  50. [58]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

  51. [59]

    sn-vancouver-num.bst

    FUNCTION identify.vancouver.version "sn-vancouver-num.bst" " [2024/07/19 v1.1 Vancouver bibliography style]" * top ENTRY address assignee author booktitle chapter cartographer day edition editor howpublished institution inventor journal key keywords month note number organizat...

    2024

  52. [60]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

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