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Proposal pack · RO-LD08-01 v1

Does industry funding or data access change what research on AI's social effects finds?

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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-ld08-01/v1/.

Causal mechanism not isolated A PhD project Needs a budget (medium or higher) No study found

Research question

Among published studies of AI's social effects (labour, information, bias, education), do studies funded by AI developers, or relying on their data or access, reach systematically different conclusions from comparable independent studies, after accounting for topic, design and venue?

Why it matters

The debate documents structural dependence through people, compute, money, data and access, but no study tests whether it changes findings. The presence-not-capture position (P5) survives only because this test has not been run. In medicine, meta-research has compared trial conclusions by sponsor (background knowledge, not a source checked for this debate); nothing comparable exists for research on AI's effects, and conflict-of-interest statements in machine-learning papers are almost absent, so funding must be traced from other records.

The debate lists this among what we still do not know: Does industry funding, data or access change what research on AI's social effects concludes?

Evidence that motivates it

  1. In 10,772 NeurIPS, ICML and CVPR papers (2015–2019), only three contained an explicit conflict-of-interest statement, although 15–20% of purely academic papers named a company in their acknowledgements; industry and academic papers differed by only 3.1% in their use of social-impact terms, academic papers using them slightly more. (LD08-SL-C09 v1.0)
    Three explicit conflict-of-interest statements in 10,807 machine-learning papers.
    Cites: Hagendorff & Meding (2021), “Ethical considerations and…”
  2. In July 2026 Anthropic committed $200 million to an Economic Futures Research Fund for external research on interventions to prepare society for AI’s economic impacts, mainly in projects of $5–30 million, supporting research “on interventions proposed in the EPF,” its own Economic Policy Framework, and on other open questions; its public page says it wants partners “willing to share what they’re learning publicly at key milestones” and states no terms on publication rights or review. (LD08-SL-C32 v1.0)
    A developer's $200 million fund supports research partly on interventions in its own policy framework; its page wants milestone sharing but states no publication terms.
    Cites: Anthropic (2026), “A research agenda for the Economic…”
  3. In a sample of 100 EMNLP 2022 papers, 44% had at least one industry-affiliated author, against 24% reported for 2019–2021 by earlier work, and citations of industry-made datasets, pre-trained models and previous best results were at least three times what the industry share of the field would predict; the authors state that this “is not proof of industry capture.” (LD08-SL-C06 v1.0)
    Heavy reliance on industry artifacts, which the authors say is not proof of capture.
    Cites: Aitken et al. (2024), “Collaboration or Corporate…”
  4. Bak-Coleman, O’Connor, Bergstrom and West argue that technology firms shape research through control of data and funding: in the Meta 2020 US election studies “all experimental designs had to be approved by Meta,” and while Meta lists more than a thousand people it funded through open awards or fellowships, OpenAlex shows only 472 publications by independent academics acknowledging Facebook or Meta as a funder, a gap the authors say should not be read as a deliberate attempt to obscure funding. (LD08-SL-C31 v1.0)
    A perspective essay reports a gap between funded individuals and acknowledged funding.
    Cites: Bak-Coleman et al. (2025), “The Risks of Industry…”

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

A possible design

Build a corpus of empirical studies of AI's social effects (2020–2026) with pre-registered coding of conclusions (direction and strength of harms or benefits found) by coders blind to funding. Code funding, data source and access route from acknowledgements, grant databases, CVs and developer funding announcements. Compare conclusions by funding and data source within topic and design, and test sensitivity to undisclosed funding using the gap between announced grantees and acknowledgements.

Data needed

Study corpus with full texts; grant and fellowship lists published by developers and foundations; author CVs; a blinded coding protocol.

Feasibility

A PhD project. Medium; mostly coder time for blinded coding and funding tracing. Undisclosed funding can bias the comparison in either direction, because disclosure may depend on what a study finds; the design gives no basis for a bound, so report sensitivity analyses over plausible disclosure patterns. Topic selection by funders is itself a finding and should be measured separately from conclusions.

Has this been done?

The search found no study that does the core design (first-pass screen of 16 October 2026; not yet checked in depth).

No study was found that compares the conclusions of research on AI's social effects by funding or data source. Nearest work counts industry affiliations, funding and citations in technical venues, or argues by analogy with other industries.

Where the search could not reach. Screened by two web searches and an arXiv record lookup on the date shown; Scopus, Web of Science and meta-research journals 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_01_v1,
  author = {{SocioLens project}},
  title = {Does industry funding or data access change what research on AI's social effects finds?},
  year = {2026},
  date = {2026-10-16},
  howpublished = {\url{https://sociolens-web.pages.dev/opportunities/ro-ld08-01/v1/}},
  note = {Research opportunity RO-LD08-01, version v1}
}

@misc{ro_ld08_01_nearest_1,
  title = {Gnewuch et al., funding and citation study (LD08-SL-C07)},
  url = {https://arxiv.org/abs/2512.05714},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld08_01_nearest_2,
  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{ro_ld08_01_nearest_3,
  title = {Abdalla \& Abdalla, analogy with the tobacco industry (LD08-SL-C08)},
  url = {https://arxiv.org/abs/2009.13676},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_hagendorff_meding_ai_society_2021,
  title = {Hagendorff \& Meding, “Ethical considerations and statistical analysis of industry involvement in machine learning research,” AI \& Society, 2021},
  url = {https://doi.org/10.1007/s00146-021-01284-z},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_anthropic_economic_futures_fund_2026_07_22,
  title = {Anthropic, “A research agenda for the Economic Futures Research Fund,” 22 July 2026, as served on 16 October 2026},
  url = {https://www.anthropic.com/news/economic-futures-research-fund-agenda},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_aitken_et_al_acl_2024,
  title = {Aitken, Abdalla, Rudie \& Stinson, “Collaboration or Corporate Capture? Quantifying NLP’s Reliance on Industry Artifacts and Contributions,” ACL 2024},
  url = {https://arxiv.org/abs/2312.03912},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_bak_coleman_et_al_arxiv_2510_19894,
  title = {Bak-Coleman, O’Connor, Bergstrom \& West, “The Risks of Industry Influence in Tech Research,” arXiv:2510.19894, 2025},
  url = {https://arxiv.org/abs/2510.19894},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

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