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Proposal pack · RO-LD07-07 v2

How do research ethics boards judge synthetic participants and AI-interpreted data?

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Read this first. This is a research hypothesis drafted by Claude (SocioLens Project Lead), AI-drafted from the LD-07 debate on 12 October 2026, not a finding. Checked by Claude (SocioLens Project Lead) with AI research agents, by web search; not a person and not a domain expert; an independent AI reviewer with fresh context (not a person), 12 October 2026: every nearest-work citation checked against its source page; corrections applied. Every check so far was done by AI; no person or domain expert has checked it yet. The literature screen below was done by AI web search and can miss relevant work. Version v2, permanent address https://sociolens-web.pages.dev/opportunities/ro-ld07-07/v2/.

Institutional practice unknown One PhD chapter or paper Low cost Partly addressed

Research question

How do ethics-board guidance documents across countries classify synthetic participants, LLM coding or interpretation of qualitative data, and transfer of participant data to commercial model APIs, and in a factorial vignette, do board members' decisions and requested safeguards vary with data sensitivity, model hosting and the degree of delegated interpretation?

Why it matters

Researchers are already submitting protocols that use these methods, and decisions currently depend on which board they face. Evidence on how boards reason would help researchers design approvable studies and help institutions write consistent guidance.

The debate lists this among what we still do not know: How should research ethics boards evaluate delegated interpretation, synthetic participants and confidential-data use?

Evidence that motivates it

  1. Human–LLM agreement is not a sufficient decision rule for qualitative coding quality, and current evidence does not establish LLM replacement of researcher interpretation in inductive qualitative analysis. (LD07-HQ-C15 v1.0)
    Agreement metrics do not establish that interpretation can be delegated, which is exactly what boards must weigh.
    Cites: Liu et al., “Agreement Is Not Quality: Blind Expert…”; “Large language models for thematic analysis in healthcare…”
  2. LLMs can support codebook-driven and deductive qualitative coding in researcher-supervised workflows, reducing coding burden without establishing autonomous interpretive validity. (LD07-HQ-C14 v1.0)
    Supervised, codebook-driven LLM coding is the defensible use boards are likely to see.
    Cites: Than et al. (2025), “Updating ‘The Future of Coding’…”; “Large language models for thematic analysis in healthcare…”
  3. Synthetic respondents can match selected survey averages while failing to reproduce real-world variance, regression relationships, individual structure and subgroup heterogeneity. (LD07-HQ-C06 v1.0)
    Synthetic respondents do not substitute for populations, which bears on whether they reduce or relocate research risk.
    Cites: Bisbee et al. (2024), “Synthetic Replacements for Human…”; Xie et al. (2026), “Evaluating the statistical realism of…”; Wang et al. NMI 2025; Gong Weigang & Huang Siyuan (2025), “整体事实还是有偏样本——基于大语言模型生成…”; Ling Wanying & Cui Sizhan (2025), “复现的限度——‘硅基样本’的可能与可为”

Claims are shown as they read on the day this pack was built; claim pages keep every earlier version.

A possible design

Part one: collect and code published guidance from university ethics boards and national bodies in several countries. Part two: a factorial vignette survey of board members varying data sensitivity, API versus local hosting, synthetic versus human participants, and the share of interpretation delegated; outcomes are approval, conditions and stated reasons.

Data needed

Public guidance documents; access to board members through professional associations; a short online survey.

Feasibility

One PhD chapter or paper. Low: document analysis and a small online survey. Suits sociology of science, research ethics or STS students; recruiting board members is the main constraint.

Has this been done?

Partly done already: related work covers some of this question (first-pass screen of 12 October 2026, checked again on 13 October 2026).

Empirical work on ethics boards and AI has begun: a 2026 survey of Canadian university REBs on web scraping and AI, a 2026 interview study of South African REC members on LLMs, and a coding of US R1 universities' researcher guidance on generative AI that flags uploading human-subjects data. No study found classifies ethics-board guidance across countries on synthetic participants, LLM coding or interpretation of qualitative data and transfer of participant data to commercial model APIs, or tests experimentally how board members' decisions and requested safeguards vary with data sensitivity, hosting and the share of interpretation delegated.

Literature check. This round found empirical work on ethics boards and AI that the first pass missed: Cappello & Stinson (JERHRE 2026) surveyed Canadian university REBs on web scraping and AI, Obasa & Kabanda (BMC Medical Ethics 2026) interviewed South African REC members about LLMs, and Ganguly et al. (AI and Ethics 2025) coded US R1 researcher guidance, including warnings on uploading human-subjects data. None classifies guidance on the three specific methods (synthetic participants, LLM interpretation of qualitative data, transfer to commercial APIs) across countries, and none runs a factorial vignette of board decisions, so the core design stands, but related work is no longer thin and the verdict moves from OPEN to PARTLY_ADDRESSED. Checked by Claude (SocioLens Project Lead) with AI research agents, by web search; not a person and not a domain expert.

Where the search could not reach. Screened by web search on the date shown; Google Scholar, SSRN and paywalled journal pages were only partly reachable. Absence of a hit is not proof that the work does not exist. Literature check of 2026-10-13: The Cappello & Stinson article page (Sage) and its Queen's repository record could not be opened, so its scope and whether it includes LLM-specific items or vignettes are unconfirmed and should be checked by hand. Tandfonline policy-analysis article (doi:10.1080/00987913.2025.2581429) could not be opened. The 2026 PubMed record flagged in the first pass was not identified. Non-English guidance search returned nothing useful.

Before building a project on it, run your own search in a scholarly database such as Scopus, Web of Science, Google Scholar or SSRN.

Nearest work found

References

Sources behind the motivating claims:

BibTeX

The first entry cites this opportunity; then its nearest work; then the sources above. Nearest work and sources are recorded as reference strings, so each goes in whole as the title; add authors and year from the original before you cite it.

@misc{sociolens_ro_ld07_07_v2,
  author = {{SocioLens project}},
  title = {How do research ethics boards judge synthetic participants and AI-interpreted data?},
  year = {2026},
  date = {2026-10-13},
  howpublished = {\url{https://sociolens-web.pages.dev/opportunities/ro-ld07-07/v2/}},
  note = {Research opportunity RO-LD07-07, version v2}
}

@misc{ro_ld07_07_nearest_1,
  title = {Hosseini, Eisenman, Riddle et al., "Guidelines needed for the use of AI in the preparation or review of IRB, IBC, and IACUC applications," Accountability in Research 33(5) (2026)},
  url = {https://pmc.ncbi.nlm.nih.gov/articles/PMC12823195},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld07_07_nearest_2,
  title = {Singer Pressman, "When Artificial Intelligence Enters the Research Process: Implications for Data Integrity in Human Participants Research," JERHRE 21(4) (2026)},
  url = {https://journals.sagepub.com/doi/10.1177/15562646261453181},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld07_07_nearest_3,
  title = {Ng et al., "Understanding How University Guidelines Address Privacy and Security Issues of Generative AI in Academic Settings," arXiv:2506.20463 (2025)},
  url = {https://arxiv.org/abs/2506.20463},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld07_07_nearest_4,
  title = {Cappello \& Stinson, "The Challenges of Web Scraping and AI for Research Ethics in Canada: A Survey of University REBs," Journal of Empirical Research on Human Research Ethics (2026), doi:10.1177/15562646261434135},
  url = {https://journals.sagepub.com/doi/10.1177/15562646261434135},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only; Some details not confirmed}
}

@misc{ro_ld07_07_nearest_5,
  title = {Obasa \& Kabanda, "Research Ethics Committees (RECs) perspectives on large language models and AI ethics review: a South African case," BMC Medical Ethics 27:206 (2026)},
  url = {https://link.springer.com/article/10.1186/s12910-026-01471-x},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld07_07_nearest_6,
  title = {Ganguly, Johri, Ali, McDonald et al., "Generative artificial intelligence for academic research: evidence from guidance issued for researchers by higher education institutions in the United States," AI and Ethics 5:3917-3933 (2025)},
  url = {https://link.springer.com/article/10.1007/s43681-025-00688-7},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld07_07_nearest_7,
  title = {Babamahmoodi, "From Ethical Principles to Lifecycle Oversight: A Library-Based Analysis of AI-Enabled Health Research in Canada," Research Square preprint rs-10864198/v1 (2026)},
  url = {https://www.researchsquare.com/article/rs-10864198/v1},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld07_07_nearest_8,
  title = {Eto, Miller, Vidal \& Lifson, "Streamlining IRB review of AI human subjects research (AIHSR): the three-stage framework," Frontiers in Systems Biology (2026), doi:10.3389/fsysb.2026.1804193},
  url = {https://www.frontiersin.org/journals/systems-biology/articles/10.3389/fsysb.2026.1804193/full},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_ld07_liu_agreement_is_not_quality_2026,
  title = {Liu et al., “Agreement Is Not Quality: Blind Expert Verification of Human and LLM Qualitative Coding When Human Consensus Is Not Ground Truth,” arXiv:2607.28890},
  url = {https://arxiv.org/abs/2607.28890},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_ld07_plos_thematic_analysis_2026,
  title = {“Large language models for thematic analysis in healthcare research: A blinded mixed-methods comparison with human analysts,” PLOS Digital Health 2026},
  url = {https://doi.org/10.1371/journal.pdig.0001189},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_ld07_than_future_of_coding_2025,
  title = {Than, Fan, Law, Nelson \& McCall, “Updating ‘The Future of Coding’: Qualitative Coding with Generative Large Language Models,” Sociological Methods \& Research 54(3):849–888 (2025)},
  url = {https://doi.org/10.1177/00491241251339188},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_ld07_bisbee_synthetic_replacements_2024,
  title = {Bisbee, Clinton, Dorff, Kenkel \& Larson, “Synthetic Replacements for Human Survey Data? The Perils of Large Language Models,” Political Analysis 32(4):401–416 (2024)},
  url = {https://doi.org/10.1017/pan.2024.5},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_ld07_xie_ssdatabench_2026,
  title = {Xie et al., “Evaluating the statistical realism of LLM-generated social science data,” PNAS 123(19):e2538145123 (2026)},
  url = {https://doi.org/10.1073/pnas.2538145123},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_r03_wang_morgenstern_dickerson_nmi_2025,
  title = {Wang et al. NMI 2025},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_ld07_gong_huang_cgss_2025,
  title = {Gong Weigang \& Huang Siyuan, “整体事实还是有偏样本——基于大语言模型生成数据的测量,” Academic Monthly 57(6):123–136 (2025)},
  url = {https://www.xsyk021.com/article/id/4c1886eb-c385-4019-92ff-d1cd75547fa5},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_ld07_ling_cui_silicon_samples_2025,
  title = {Ling Wanying \& Cui Sizhan, “复现的限度——‘硅基样本’的可能与可为,” Journal of Intelligent Society 4(2):179–201 (2025)},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

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