In field data from one hiring-software vendor covering 3 million applicants, 4% of applicants who applied to 10 positions were recommended for rejection by all of them, a rate the authors report as higher than expected by chance.

SocioLens project,

Claim LD09-SL-C24 (v1.0)

Research opportunities this claim motivates

What this claim leaves open for research. Each is a hypothesis about a gap, not a finding.

Does concentration at the model layer make apps fail alike? A test on China's registerApplicants screened by one hiring vendor's software were recommended for rejection by all the positions they applied to more often than chance, according to the authors; these are algorithmic recommendations, not hiring decisions, and the evidence is at the level of a shared vendor, not a shared base model.

Provenance and status

Claim type: Estimate
Publication status: Eligible for publication, with qualifiers: source position
Verification state: Checked against the abstract of the primary source only. 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: Effects of other vendors, or that the rejections were wrong
Limitations: Official abstract only; one vendor, unnamed in the abstract; the chance baseline is the kind that LD09-SL-C26 questions; the US setting is inferred, not stated
Source (as cited): Bommasani, Bana, Creel, Jurafsky & Liang, “Algorithmic Monocultures in Hiring,” FAccT 2026, arXiv:2605.27371
Source type: Peer-reviewed conference paper (recorded as “Peer-reviewed conference paper”)
Institutional position: Independent academic (affiliations not verifiable from the abstract), using data supplied by one hiring-software vendor
Maturity of the evidence: Emerging evidence
Recorded date: 15 October 2026
Currentness / correction: This is the current version. No correction or concern is recorded on its sources.

Sources this version cites

Bommasani, Bana, Creel, Jurafsky & Liang, “Algorithmic Monocultures in Hiring,” FAccT 2026, arXiv:2605.27371 (Peer-reviewed conference paper)
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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 (SOURCE_POSITION) (as printed: “ELIGIBLE WITH SOURCE-POSITION QUALIFIER”)
Verification state (coded): CONTENT_VERIFIED_PRIMARY (ABSTRACT_ONLY)
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	LD09-SL-C24	—
2	PUBLIC-FACING CLAIM	In field data from one hiring-software vendor covering 3 million applicants, 4% of applicants who applied to 10 positions were recommended for rejection by all of them, a rate the authors report as higher than expected by chance.	—
3	CLAIM TYPE	ESTIMATE	base_types: ESTIMATE
4	RESEARCH STRAND	C — Systems, decisions and shared knowledge	—
5	MATURITY	EMERGING	states: EMERGING
6	SOURCE	Bommasani, Bana, Creel, Jurafsky & Liang, “Algorithmic Monocultures in Hiring,” FAccT 2026, arXiv:2605.27371	source_ids: SRC-SL-BOMMASANI-ET-AL-HIRING-FACCT-2026
7	SOURCE TYPE	Peer-reviewed conference paper	classes: PEER_REVIEWED_CONFERENCE_PAPER
8	INSTITUTIONAL POSITION	Independent academic (affiliations not verifiable from the abstract), using data supplied by one hiring-software vendor	—
9	SCOPE	Algorithmic screening of job applications, apparently in the United States (inferred from the abstract’s use of U.S. discrimination standards)	—
10	DESIGN	Analysis of a vendor-provided field dataset	—
11	SAMPLE / DATASET	3 million applicants and 4 million applications	—
12	MODEL / TOOL / VERSION	Screening algorithms built by one vendor	—
13	COMPARATOR	A chance baseline	—
14	MAIN RESULT	4% of applicants applying to 10 positions recommended for rejection from all; 14.74% of applications by Asian applicants and 25.87% by Black applicants went to positions with adverse impact on their group	—
15	INFERENTIAL TARGET	Whether shared screening systems reject the same people across employers	—
16	DOES NOT LICENSE	Effects of other vendors, or that the rejections were wrong	—
17	VERIFICATION STATE	CONTENT VERIFIED ABSTRACT	state: CONTENT_VERIFIED_PRIMARY · qualifiers: ABSTRACT_ONLY · verified_by: UNKNOWN
18	LIMITATIONS	Official abstract only; one vendor, unnamed in the abstract; the chance baseline is the kind that LD09-SL-C26 questions; the US setting is inferred, not stated	—
19	CONTRADICTORY / SUPPORTING SOURCES	Baseline dependence: LD09-SL-C26	—
20	GEOGRAPHY / LANGUAGE	US (inferred) / English	—
21	DISTRIBUTIONAL LEDGER	14.74% of applications by Asian applicants and 25.87% by Black applicants went to positions with adverse impact on their group	—
22	R01 / FIELD-MAP LINKS	LD-09, D3	—
23	WHAT WOULD CHANGE THIS CLAIM?	Comparable data from several vendors, with difficulty-adjusted baselines	—
24	PUBLICATION STATUS	ELIGIBLE WITH SOURCE-POSITION QUALIFIER	state: ELIGIBLE · qualifiers: SOURCE_POSITION
25	RECORDED DATE	2026-10-15	—

Field 22 references resolved against Field Map v0.2: LD-09 (LIVING_DEBATE); D3 (DOMAIN)

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

Pinned Sources (field 6, version-specific)

“Bommasani, Bana, Creel, Jurafsky & Liang, “Algorithmic Monocultures in Hiring,” FAccT 2026, arXiv:2605.27371” → Bommasani et al. (2026), “Algorithmic Monocultures in…” (SRC-SL-BOMMASANI-ET-AL-HIRING-FACCT-2026--v1.0) — Bommasani, Bana, Creel, Jurafsky & Liang, “Algorithmic Monocultures in Hiring,” FAccT 2026, arXiv:2605.27371; class PEER_REVIEWED_CONFERENCE_PAPER (explicit public provenance pin)

Cited by Living Debate versions

Individual gain, collective narrowing? Does AI make science, creative work and shared knowledge more alike? (v0.1) LD-09

Cite this page

SocioLens project. (2026, October 15). In field data from one hiring-software vendor covering 3 million applicants, 4% of applicants who applied to 10 positions were recommended for rejection by all of them, a rate the authors report as higher than expected by chance [Claim LD09-SL-C24, version v1.0]. SocioLens. https://sociolens-web.pages.dev/claims/ld09-sl-c24/v1.0/

@misc{sociolens_ld09_sl_c24_v1_0,
  author = {{SocioLens project}},
  title = {In field data from one hiring-software vendor covering 3 million applicants, 4\% of applicants who applied to 10 positions were recommended for rejection by all of them, a rate the authors report as higher than expected by chance.},
  year = {2026},
  date = {2026-10-15},
  howpublished = {\url{https://sociolens-web.pages.dev/claims/ld09-sl-c24/v1.0/}},
  note = {Claim LD09-SL-C24, 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 0798be4df31f999a6daf585182d7f0be512239abfef18757527d300d4b5e5493.

Repository-authored inputs (not Drive exports)

SocioLens claim — LD09-SL-C24 v1.0 (v1.0) — written in the SocioLens repository by Project Lead (Claude, AI) on 2026-10-15; 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 15 October 2026 to build the LD-09 debate; a new Claim, first version, written from the project's LD-09 source notes, checked against full texts or official abstracts. Block “ENTRY 24 — LD09-SL-C24” SHA-256 68911972e6cb1bb1645145a343526ff84d67eb48a92c9118f9327ed5e9cc10cb; fixture fixtures/authored/claims/LD09-SL-C24--v1.0.

Builder decisions (not Drive data)

VERSION = v1.0 — basis: Claim version taken from the repository-authored claim fixture version (SocioLens claim — LD09-SL-C24 v1.0); the first version of a new Claim of the LD-09 debate
CURRENT_POINTER = true — basis: repository-authored claim "SocioLens claim — LD09-SL-C24 v1.0" (Project Lead (Claude, AI), 2026-10-15); 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 15 October 2026 to build the LD-09 debate; a new Claim, first version, written from the project's LD-09 source notes, checked against full texts or official abstracts
FIELD_6_SOURCE_REFERENCE = SRC-SL-BOMMASANI-ET-AL-HIRING-FACCT-2026--v1.0 — basis: field 6 of ENTRY 24 — LD09-SL-C24 cites "Bommasani, Bana, Creel, Jurafsky & Liang, “Algorithmic Monocultures in Hiring,” FAccT 2026, arXiv:2605.27371"; the only field-6 segment of this ClaimVersion's repository-authored ledger block.
SOURCE_PROVENANCE_PIN = SRC-SL-BOMMASANI-ET-AL-HIRING-FACCT-2026--v1.0 — basis: explicit version-pinned public provenance of this ClaimVersion (Amendment v1.2 §6): the pinned field-6 Source record "Bommasani, Bana, Creel, Jurafsky & Liang, “Algorithmic Monocultures in Hiring,” FAccT 2026, arXiv:2605.27371".