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Proposal pack · RO-LD08-05 v1

Who writes research on AI's social effects outside arXiv?

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Read this first. This is a research hypothesis drafted by Claude (SocioLens Project Lead), AI-drafted from the LD-08 debate on 16 October 2026, not a finding. Not yet reviewed. 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 v1, permanent address https://sociolens-web.pages.dev/opportunities/ro-ld08-05/v1/.

Not yet measured One PhD chapter or paper Low to medium cost Partly addressed

Research question

Across social-science and humanities journals, books, policy reports and arXiv, what share of research on AI's social effects comes from computer-science teams, social scientists, mixed teams, industry and government, and how has that share changed since 2014?

Why it matters

The claim that computer science is absorbing the study of AI's social effects (the jurisdiction position, P4) rests mainly on arXiv papers; the main study excludes social-science journals. If journals, books and policy reports tell a different story, the debate's jurisdiction picture changes. The disciplinary map outside arXiv is not measured.

The debate lists this among what we still do not know: Is computer science absorbing research on AI's social effects outside technical preprint servers?

Evidence that motivates it

  1. In about 102,000 arXiv AI papers (2014–2024), the share of societally oriented sentences written by computer-science-only teams rose from 49.0% to 71.2%, while the share written by teams including social scientists or humanists fell from 25.7% to 3.8%, although such teams wrote the most societally oriented papers (20.9% of sentences, against 7.8% for computer-science-only teams). (LD08-SL-C12 v1.0)
    The rise of computer-science-only teams is measured on arXiv papers only.
    Cites: Markus et al. (2025), “Societal AI Research Has Become…”
  2. Of the papers published at the FAccT conference from 2018 to 2021, 61.3% were labelled STEM and 29.7% humanities, social sciences and arts; summarising a survey of conference affiliates, the authors list concerns including “out-sized industry connections.” (LD08-SL-C13 v1.0)
    The disciplinary mix of one conference, 2018 to 2021.
    Cites: Laufer et al. (2022), “Four Years of FAccT: A Reflexive…”
  3. Of 84 AI ethics guidelines collected worldwide by 2019, private companies issued the largest share (19, or 22.6%), ahead of governmental agencies (18) and academic and research institutions (9). (LD08-SL-C14 v1.0)
    Companies issued the largest share of AI ethics guidelines collected by 2019.
    Cites: Jobin et al. (2019), “The global landscape of AI ethics…”
  4. In April 2025 Anthropic set up an Economic Advisory Council of “distinguished economists” to advise it on AI’s economic effects and to “inform the research agenda for the Anthropic Economic Index.” (LD08-SL-C35 v1.0)
    A developer's council of economists informs the agenda of its usage research.
    Cites: Anthropic (2025), “Introducing the Anthropic Economic…”

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

A possible design

Draw a stratified sample of publications on AI's social effects from journal databases, book catalogues, policy-report repositories and arXiv (2014–2026), code authors' disciplines and sectors from affiliations and CVs, and estimate shares by venue type and year. Use the same coding for arXiv as the existing arXiv study so the two can be compared.

Data needed

Bibliographic records from journal and book databases; policy-report collections; affiliation and CV data; a coding protocol for discipline and sector.

Feasibility

One PhD chapter or paper. Low to medium; database access and coding time. Database coverage favours English-language journals; report language coverage alongside the shares.

Has this been done?

Partly done already: related work covers some of this question (first-pass screen of 16 October 2026; not yet checked in depth).

Bibliometric studies count AI publications in the social sciences, and one study measures team composition on arXiv, but no study was found that compares who writes on AI's social effects across arXiv, journals, books and policy reports.

Where the search could not reach. Screened by web searches and arXiv record lookups on the date shown; Scopus, Web of Science and book catalogues were not searched. 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

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_ld08_05_v1,
  author = {{SocioLens project}},
  title = {Who writes research on AI's social effects outside arXiv?},
  year = {2026},
  date = {2026-10-16},
  howpublished = {\url{https://sociolens-web.pages.dev/opportunities/ro-ld08-05/v1/}},
  note = {Research opportunity RO-LD08-05, version v1}
}

@misc{ro_ld08_05_nearest_1,
  title = {arXiv team-composition study (LD08-SL-C12)},
  url = {https://arxiv.org/abs/2506.08738},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld08_05_nearest_2,
  title = {Prieto-Gutierrez, Segado-Boj \& Da Silva França, "Artificial intelligence in social science: A study based on bibliometrics analysis," Human Technology 19(2) (2023); arXiv:2312.10077},
  url = {https://arxiv.org/abs/2312.10077},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{src_sl_markus_gilardi_stetsenko_arxiv_2506_08738,
  title = {Markus, Gilardi \& Stetsenko, “Societal AI Research Has Become Less Interdisciplinary,” arXiv:2506.08738, 2025},
  url = {https://arxiv.org/abs/2506.08738},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_laufer_et_al_facct_2022,
  title = {Laufer, Jain, Cooper, Kleinberg \& Heidari, “Four Years of FAccT: A Reflexive, Mixed-Methods Analysis of Research Contributions, Shortcomings, and Future Prospects,” FAccT 2022},
  url = {https://doi.org/10.1145/3531146.3533107},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_jobin_ienca_vayena_nmi_2019,
  title = {Jobin, Ienca \& Vayena, “The global landscape of AI ethics guidelines,” Nature Machine Intelligence, 2019},
  url = {https://doi.org/10.1038/s42256-019-0088-2},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_anthropic_economic_advisory_council_2025_04_28,
  title = {Anthropic, “Introducing the Anthropic Economic Advisory Council,” 28 April 2025 (updated 9 May 2025), as served on 16 October 2026},
  url = {https://www.anthropic.com/news/introducing-the-anthropic-economic-advisory-council},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

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