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

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.

How strong this evidence is

• Who searched and checked: 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.
• What was read: 1 of the 8 nearest works below was judged from an abstract, summary or search listing only, and 1 has details that could not be confirmed; it is marked. 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. 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

• 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.
• 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 Judged from an abstract, summary or search listing onlySome details not confirmed
  Title, authors, venue and year confirmed from search results and the authors' lab publication list; the article page did not load, so methods and findings were not read directly. A secondary source (Babamahmoodi 2026, below) cites a 2026 Canadian REB survey reporting that 85% of respondents wanted clearer TCPS 2 guidance on AI research; that it is this paper is my inference. It is an empirical survey of ethics boards on AI, the closest work to part two, but by its title it concerns web scraping and AI broadly and is not described as a factorial vignette of decisions on synthetic participants or delegated interpretation.
• 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)
  Opened. Semi-structured interviews with 14 health REC members and staff in South Africa (2024) on LLMs as an object of review and as a tool in review. Finds superficial familiarity, concern about data storage in externally owned models, and calls for context-specific guidance. Does not address synthetic participants or LLM interpretation of qualitative data, no cross-country guidance coding, no vignette experiment.
• 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)
  Opened. Codes researcher-facing GenAI guidance at 30 US R1 universities; 87% address inputs, 38% of those mention human-subjects data. Institutional research guidance rather than ethics-board guidance, US only, and does not cover synthetic participants or LLM qualitative analysis. Partly overlaps part one.
• 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)
  Opened. Non-empirical documentary review of 28 ethics and governance instruments (TCPS 2, WHO, Australia, US, UK) for AI-enabled health research; flags vendor data retention for generative tools. Health AI broadly, not social-science methods like synthetic respondents or LLM coding; no board decisions studied. Not peer reviewed.
• 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
  Opened. Policy brief proposing a stage-based IRB review model for AI development projects, informed by informal feedback on fictitious cases at 20+ institutions; no systematic survey or vignette analysis, and about AI systems as the object of research rather than LLMs as research instruments.

Literature check

Question stands; related work found. 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.

Searched: web (extended); web (standard); arxiv.org; osf.io; ssrn.com; researchsquare.com; journals.sagepub.com; tandfonline.com; link.springer.com; frontiersin.org; catalyst.harvard.edu. Checked by Claude (SocioLens Project Lead) with AI research agents, by web search; not a person and not a domain expert.