In 100 highly cited NeurIPS and ICML papers, the share with ties to large technology firms (author affiliation or funding) rose from 21% in 2008/09 to 66% in 2018/19, and the share with any corporate tie from 45% to 79%; 15% of the papers justified their work by a societal need and 1% discussed potential negative effects.

SocioLens project,

Claim LD08-SL-C04 (v1.0)

Provenance and status

Claim type: Estimate
Publication status: Eligible for publication, with a qualifier
Verification state: Checked against the primary source. Who checked this claim is not recorded for it; claim checks so far were AI-led, and no person has checked claims against their sources.
Does not license: A claim about research on AI’s social effects, or that corporate ties caused the values found
Limitations: Read in arXiv version 2; small sample of the most cited papers, which may favour industry work; machine-learning research, not research on AI’s social effects
Source (as cited): Birhane, Kalluri, Card, Agnew, Dotan & Bao, “The Values Encoded in Machine Learning Research,” FAccT 2022
Source type: Peer-reviewed conference paper (recorded as “Peer-reviewed conference paper”)
Institutional position: Independent academic (with a Mozilla Foundation fellow)
Maturity of the evidence: Emerging evidence
Recorded date: 16 October 2026
Currentness / correction: This is the current version. No correction or concern is recorded on its sources.

Sources this version cites

Birhane, Kalluri, Card, Agnew, Dotan & Bao, “The Values Encoded in Machine Learning Research,” FAccT 2022 (Peer-reviewed conference paper)
Read the original

Version

This is the current version, v1.0. Earlier versions stay available, and their records never change.

Versions: v1.0 · current

Raw record: every recorded field, with its coded values

All 25 canonical Claim fields, verbatim, with the coded values derived from them. The plain-language labels above are read from these.

Claim type (coded): ESTIMATE (as printed: “ESTIMATE”)
Publication status (coded): ELIGIBLE (QUALIFIED) (as printed: “ELIGIBLE WITH QUALIFIER”)
Verification state (coded): CONTENT_VERIFIED_PRIMARY
Source type (coded): PEER_REVIEWED_CONFERENCE_PAPER
Maturity (coded): EMERGING

#	Canonical field	Canonical value (verbatim)	Normalized (Claim Value Vocabulary v1.0.2)
1	CLAIM ID	LD08-SL-C04	—
2	PUBLIC-FACING CLAIM	In 100 highly cited NeurIPS and ICML papers, the share with ties to large technology firms (author affiliation or funding) rose from 21% in 2008/09 to 66% in 2018/19, and the share with any corporate tie from 45% to 79%; 15% of the papers justified their work by a societal need and 1% discussed potential negative effects.	—
3	CLAIM TYPE	ESTIMATE	base_types: ESTIMATE
4	RESEARCH STRAND	A — Who produces knowledge about AI	—
5	MATURITY	EMERGING	states: EMERGING
6	SOURCE	Birhane, Kalluri, Card, Agnew, Dotan & Bao, “The Values Encoded in Machine Learning Research,” FAccT 2022	source_ids: SRC-SL-BIRHANE-ET-AL-VALUES-FACCT-2022
7	SOURCE TYPE	Peer-reviewed conference paper	classes: PEER_REVIEWED_CONFERENCE_PAPER
8	INSTITUTIONAL POSITION	Independent academic (with a Mozilla Foundation fellow)	—
9	SCOPE	Highly cited machine-learning papers from two leading venues, 2008/09 and 2018/19	—
10	DESIGN	Line-by-line annotation of paper text for values, plus coding of author affiliations and funding acknowledgements	—
11	SAMPLE / DATASET	100 papers (50 per period)	—
12	MODEL / TOOL / VERSION	Not applicable	—
13	COMPARATOR	The earlier period (2008/09)	—
14	MAIN RESULT	Big Tech ties 21% to 66%; any corporate tie 45% to 79%; university ties fell to 81%; 15% cite a societal need; 1% discuss negative potential	—
15	INFERENTIAL TARGET	Corporate presence in, and the stated values of, influential machine-learning research	—
16	DOES NOT LICENSE	A claim about research on AI’s social effects, or that corporate ties caused the values found	—
17	VERIFICATION STATE	CONTENT VERIFIED	state: CONTENT_VERIFIED_PRIMARY · verified_by: UNKNOWN
18	LIMITATIONS	Read in arXiv version 2; small sample of the most cited papers, which may favour industry work; machine-learning research, not research on AI’s social effects	—
19	CONTRADICTORY / SUPPORTING SOURCES	LD08-SL-C05; industry papers use social-impact language about as often as academic ones: LD08-SL-C09	—
20	GEOGRAPHY / LANGUAGE	Global / English	—
21	DISTRIBUTIONAL LEDGER	80% of the university-affiliated papers came from elite universities	—
22	R01 / FIELD-MAP LINKS	LD-08, D3	—
23	WHAT WOULD CHANGE THIS CLAIM?	A larger, venue-wide sample, or the same coding for research on AI’s social effects	—
24	PUBLICATION STATUS	ELIGIBLE WITH QUALIFIER	state: ELIGIBLE · qualifiers: QUALIFIED
25	RECORDED DATE	2026-10-16	—

Field 22 references resolved against Field Map v0.2: LD-08 (LIVING_DEBATE); D3 (DOMAIN)

The sources as recorded, with their record IDs and coded source types:

Pinned Sources (field 6, version-specific)

“Birhane, Kalluri, Card, Agnew, Dotan & Bao, “The Values Encoded in Machine Learning Research,” FAccT 2022” → Birhane et al. (2022), “The Values Encoded in Machine…” (SRC-SL-BIRHANE-ET-AL-VALUES-FACCT-2022--v1.0) — Birhane, Kalluri, Card, Agnew, Dotan & Bao, “The Values Encoded in Machine Learning Research,” FAccT 2022; class PEER_REVIEWED_CONFERENCE_PAPER (explicit public provenance pin)

Cited by Living Debate versions

Who can know about AI? Epistemic dependence and jurisdiction over the field (v0.1) LD-08

Cite this page

SocioLens project. (2026, October 16). In 100 highly cited NeurIPS and ICML papers, the share with ties to large technology firms (author affiliation or funding) rose from 21% in 2008/09 to 66% in 2018/19, and the share with any corporate tie from 45% to 79%; 15% of the papers justified their work by a societal need and 1% discussed potential negative effects [Claim LD08-SL-C04, version v1.0]. SocioLens. https://sociolens-web.pages.dev/claims/ld08-sl-c04/v1.0/

@misc{sociolens_ld08_sl_c04_v1_0,
  author = {{SocioLens project}},
  title = {In 100 highly cited NeurIPS and ICML papers, the share with ties to large technology firms (author affiliation or funding) rose from 21\% in 2008/09 to 66\% in 2018/19, and the share with any corporate tie from 45\% to 79\%; 15\% of the papers justified their work by a societal need and 1\% discussed potential negative effects.},
  year = {2026},
  date = {2026-10-16},
  howpublished = {\url{https://sociolens-web.pages.dev/claims/ld08-sl-c04/v1.0/}},
  note = {Claim LD08-SL-C04, version v1.0}
}

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.

Research-of-record provenance

Materialized by sociolens-materializer 0.6.0 at 2026-09-28T20:00:00.000Z; normalization contract SocioLens — Claim Value Vocabulary v1.0.2 — Pre-Spike Materialization Contract (v1.0.2). Content seal SHA-256 207851bac9758873949e67de26e4e643c2b0564cd8b309cb36b51cf3056ce1ee.

Repository-authored inputs (not Drive exports)

SocioLens claim — LD08-SL-C04 v1.0 (v1.0) — written in the SocioLens repository by Project Lead (Claude, AI) on 2026-10-16; no Drive identity exists. Authority: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and the Owner's choice in the SocioLens project chat on 16 October 2026 to build the LD-08 debate; a new Claim, first version, written from the project's LD-08 source notes, checked against full texts, official web pages or official abstracts. Block “ENTRY 04 — LD08-SL-C04” SHA-256 f8c662fa8e09ca77422c531946e9257c62d6d8e4a4d295fdaf98ff72602733f6; fixture fixtures/authored/claims/LD08-SL-C04--v1.0.

Builder decisions (not Drive data)

VERSION = v1.0 — basis: Claim version taken from the repository-authored claim fixture version (SocioLens claim — LD08-SL-C04 v1.0); the first version of a new Claim of the LD-08 debate
CURRENT_POINTER = true — basis: repository-authored claim "SocioLens claim — LD08-SL-C04 v1.0" (Project Lead (Claude, AI), 2026-10-16); basis: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and the Owner's choice in the SocioLens project chat on 16 October 2026 to build the LD-08 debate; a new Claim, first version, written from the project's LD-08 source notes, checked against full texts, official web pages or official abstracts
FIELD_6_SOURCE_REFERENCE = SRC-SL-BIRHANE-ET-AL-VALUES-FACCT-2022--v1.0 — basis: field 6 of ENTRY 04 — LD08-SL-C04 cites "Birhane, Kalluri, Card, Agnew, Dotan & Bao, “The Values Encoded in Machine Learning Research,” FAccT 2022"; the only field-6 segment of this ClaimVersion's repository-authored ledger block.
SOURCE_PROVENANCE_PIN = SRC-SL-BIRHANE-ET-AL-VALUES-FACCT-2022--v1.0 — basis: explicit version-pinned public provenance of this ClaimVersion (Amendment v1.2 §6): the pinned field-6 Source record "Birhane, Kalluri, Card, Agnew, Dotan & Bao, “The Values Encoded in Machine Learning Research,” FAccT 2022".