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Proposal pack · RO-LD08-02 v1

How tied to industry are researchers of AI's social effects outside computer science?

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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-ld08-02/v1/.

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

Research question

What share of authors of research on AI's social effects in social-science journals, interdisciplinary venues (FAccT, AIES) and policy reports have industry funding, employment or data agreements, how often is this disclosed, and how does it differ by discipline and region?

Why it matters

The counts of industry ties come from technical venues and from 33 AI-ethics faculty at four North American universities. Research on AI's social effects is mostly academic by authorship, but ties through funding and data would not show in affiliation counts. Without a census beyond computer science, the debate cannot say how far dependence reaches the researchers who study AI's effects, or whether Europe's lower faculty ties hold there.

The debate lists this among what we still do not know: How large are industry ties among researchers who publish on AI's social effects in social-science journals, books and policy reports?

Evidence that motivates it

  1. Among computer-science faculty at four North American universities who had published on AI ethics or fairness between January 2015 and April 2020 and whose funding sources were known, 58% (19 of 33) had at some point been directly funded by large technology firms, and 97% (32 of 33) had a financial tie to them at some stage of their careers, including graduate funding or past employment; 10 of 17 position papers on AI’s ethical or societal impact published in Nature and Science since 2015 had at least one author with such ties. (LD08-SL-C08 v1.0)
    19 of 33 AI-ethics faculty with known funding at four universities were directly funded by large firms.
    Cites: Abdalla & Abdalla (2021), “The Grey Hoodie Project: Big…”
  2. Roughly 15% or less of peer-reviewed responsible-AI publications each year had a co-author affiliated with industry; from 2018 to 2022 industry’s participation at responsible-AI conferences stayed between about 20% and 23% while at leading AI conferences it rose from 29% to 37%; 34.5% of 519 AI-publishing firms published any responsible-AI research. (LD08-SL-C11 v1.0)
    Roughly 15% or less of responsible-AI publications have an industry co-author.
    Cites: Ahmed et al. (2026), “The Narrow Depth and Breadth of…”
  3. Among faculty authors at ACL 2022 whose funding could be traced, 66% had received industry funding: 72% in the US, 69% in China and 38% in Europe; among student authors 74% had an industry scholarship or internship, including 65% in Europe. (LD08-SL-C05 v1.0)
    Industry funding among NLP faculty differs by region (US 72%, China 69%, Europe 38%).
    Cites: Abdalla et al. (2023), “The Elephant in the Room…”
  4. 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)
    FAccT is majority STEM; its affiliates raised concerns about industry connections, which were not counted.
    Cites: Laufer et al. (2022), “Four Years of FAccT: A Reflexive…”

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

A possible design

Sample authors of research on AI's social effects from 2022–2026 across FAccT, AIES, leading sociology, political-science, economics, communication and STS journals, and major policy-institute reports. For each author, code funding, employment, fellowships and data agreements with AI developers from CVs, acknowledgements, grant lists and developer announcements, and compare with disclosure statements in the papers. Report by venue type, discipline and region.

Data needed

Author lists and acknowledgements; public CVs; developer and foundation grantee lists; venue disclosure policies.

Feasibility

One PhD chapter or paper. Low to medium; manual coding of public records. CV-based coding misses undisclosed ties; double-code a subsample and report agreement.

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).

Counts exist for NLP and machine-learning venues and for a small sample of elite AI-ethics faculty; none was found for FAccT or AIES authors as a whole or for social-science journals.

Where the search could not reach. Screened by two web searches and an arXiv record lookup on the date shown; ACM Digital Library and social-science databases 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_02_v1,
  author = {{SocioLens project}},
  title = {How tied to industry are researchers of AI's social effects outside computer science?},
  year = {2026},
  date = {2026-10-16},
  howpublished = {\url{https://sociolens-web.pages.dev/opportunities/ro-ld08-02/v1/}},
  note = {Research opportunity RO-LD08-02, version v1}
}

@misc{ro_ld08_02_nearest_1,
  title = {Abdalla, Wahle, Ruas, Névéol, Ducel, Mohammad \& Fort, ACL 2023 (LD08-SL-C05)},
  url = {https://arxiv.org/abs/2305.02797},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld08_02_nearest_2,
  title = {Laufer et al., FAccT retrospective (LD08-SL-C13)},
  url = {https://arxiv.org/abs/2206.06738},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld08_02_nearest_3,
  title = {Bosten \& Kleinberg, "Conflicts of Interest in Published NLP Research 2000-2024," arXiv:2502.16218 (2025)},
  url = {https://arxiv.org/abs/2502.16218},
  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_abdalla_abdalla_grey_hoodie_aies_2021,
  title = {Abdalla \& Abdalla, “The Grey Hoodie Project: Big Tobacco, Big Tech, and the Threat on Academic Integrity,” AIES 2021},
  url = {https://arxiv.org/abs/2009.13676},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_ahmed_das_martin_banerjee_arxiv_2405_12193,
  title = {Ahmed, Das, Martin \& Banerjee, “The Narrow Depth and Breadth of Corporate Responsible AI Research,” arXiv:2405.12193, 2026},
  url = {https://arxiv.org/abs/2405.12193},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_abdalla_et_al_elephant_acl_2023,
  title = {Abdalla, Wahle, Ruas, Névéol, Ducel, Mohammad \& Fort, “The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research,” ACL 2023},
  url = {https://arxiv.org/abs/2305.02797},
  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}
}

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