Research opportunity · AI in the research apparatus
How do research ethics boards judge synthetic participants and AI-interpreted data?
In short: has this been done?
Related work: No study found. A first-pass search found no study that does the core design. It has not been checked in depth, so a fuller search could still find one.
How far checked: First-pass screen only. A first-pass AI web search on 12 October 2026. No deeper check yet. Every search and check so far was done by AI; no person or domain expert has checked them yet.
The searches, the nearest work found and what the search could not reach
Earlier version (v1)
This is RO-LD07-07 as it was published at v1, kept unchanged. The current version is v2; its changes are listed there.
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?
Gap type: Institutional practice unknownOne PhD chapter or paperLow costRelated work: No study found
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
- 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)
How this study uses it: Agreement metrics do not establish that interpretation can be delegated, which is exactly what boards must weigh. - 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)
How this study uses it: Supervised, codebook-driven LLM coding is the defensible use boards are likely to see. - 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)
How this study uses it: Synthetic respondents do not substitute for populations, which bears on whether they reduce or relocate research risk.
Each claim is shown as it read when this version was published.
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?
The search found no study that does the core design (first-pass screen of 12 October 2026; not yet checked in depth).
Found only commentary on AI and ethics review and analyses of general university AI policies. No systematic analysis of ethics-board guidance on these methods or experimental study of board decisions was found. One 2026 PubMed record could not be opened and should be checked by hand.
How strong this evidence is
- Who searched and checked: 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.
- What was read: none of the 3 nearest works below is recorded as judged from an abstract only. Not every entry says how much of the work was read.
- 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.
- 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
- 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)
Perspective on boards' own use of AI, not how they judge AI-based methods. - Singer Pressman, "When Artificial Intelligence Enters the Research Process: Implications for Data Integrity in Human Participants Research," JERHRE 21(4) (2026)
Invited commentary on AI-mediated responses for ethics boards; no empirical study of boards. - Ng et al., "Understanding How University Guidelines Address Privacy and Security Issues of Generative AI in Academic Settings," arXiv:2506.20463 (2025)
Document analysis of general university AI guidelines in 43 universities; not research ethics boards.
Search queries used
IRB research ethics committee review synthetic participants LLM simulated respondents guidancesurvey of IRB members attitudes generative AI research protocols vignette study confidential data ChatGPTdocument analysis university research ethics board policies generative AI qualitative data analysis uploading transcriptsJournal of Empirical Research on Human Research Ethics artificial intelligence IRB chairs interview survey 2025 2026
Cite this page
SocioLens project. (2026, October 12). How do research ethics boards judge synthetic participants and AI-interpreted data? [Research opportunity RO-LD07-07, version v1]. SocioLens. https://sociolens-web.pages.dev/opportunities/ro-ld07-07/v1/
@misc{sociolens_ro_ld07_07_v1,
author = {{SocioLens project}},
title = {How do research ethics boards judge synthetic participants and AI-interpreted data?},
year = {2026},
date = {2026-10-12},
howpublished = {\url{https://sociolens-web.pages.dev/opportunities/ro-ld07-07/v1/}},
note = {Research opportunity RO-LD07-07, version v1}
}The link points to this version, whose record never changes. For a finding, cite the original study as well: SocioLens summarizes research and does not replace it.
Provenance
RO-LD07-07 v1. Drafted by Claude (SocioLens Project Lead), AI-drafted from the LD-07 debate on 2026-10-12. Checked by an independent AI reviewer with fresh context (not a person), 12 October 2026: every nearest-work citation checked against its source page; corrections applied. Not yet reviewed by a domain expert. This is a research hypothesis, not a finding.