Claim LD09-SL-C23 (v1.0)
Across commercial machine-learning services tracked from 2020 to 2022, cases that every system got wrong were more common than independence would predict, and on average only 10% of one system’s improvement fell on cases all the other systems misclassified, though such cases made up 27% of the room for improvement; in a dermatology case, human dermatologists’ outcomes were more homogeneous than the models’.
Provenance and status
- Claim type
- ESTIMATE (as printed: “ESTIMATE”)
- Publication status
- ELIGIBLE (QUALIFIED) (as printed: “ELIGIBLE WITH QUALIFIER”)
- Verification state
- CONTENT_VERIFIED_PRIMARY (as printed: “CONTENT VERIFIED”; verified by: UNKNOWN)
- Does not license
- Findings about generative language models
- Source (field 6, as printed)
- Toups, Bommasani, Creel, Bana, Jurafsky & Liang, “Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes,” NeurIPS 2023, arXiv:2307.05862
- Source type (field 7)
- PEER_REVIEWED_CONFERENCE_PAPER (as printed: “Peer-reviewed conference paper”)
- Institutional position
- Independent academic (Stanford University-led)
- Maturity (claim evidence / publication maturity)
- EMERGING (as printed: “EMERGING”)
- Recorded date
- 2026-10-15
- Currentness / correction
- This is the current version. No correction or concern relation is recorded on its Sources.
Version
Current version v1.0, resolved through the explicit current pointer CLAIM--LD09-SL-C23.
Pinned Sources (field 6, version-specific)
- “Toups, Bommasani, Creel, Bana, Jurafsky & Liang, “Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes,” NeurIPS 2023, arXiv:2307.05862” → SRC-SL-TOUPS-ET-AL-NEURIPS-2023--v1.0 — Toups, Bommasani, Creel, Bana, Jurafsky & Liang, “Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes,” NeurIPS 2023, arXiv:2307.05862; class PEER_REVIEWED_CONFERENCE_PAPER (explicit public provenance pin)
All 25 canonical Claim fields (raw values and normalized values)
| # | Canonical field | Canonical value (verbatim) | Normalized (Claim Value Vocabulary v1.0.2) |
|---|---|---|---|
| 1 | CLAIM ID | LD09-SL-C23 | — |
| 2 | PUBLIC-FACING CLAIM | Across commercial machine-learning services tracked from 2020 to 2022, cases that every system got wrong were more common than independence would predict, and on average only 10% of one system’s improvement fell on cases all the other systems misclassified, though such cases made up 27% of the room for improvement; in a dermatology case, human dermatologists’ outcomes were more homogeneous than the models’. | — |
| 3 | CLAIM TYPE | ESTIMATE | base_types: ESTIMATE |
| 4 | RESEARCH STRAND | C — Systems, decisions and shared knowledge | — |
| 5 | MATURITY | EMERGING | states: EMERGING |
| 6 | SOURCE | Toups, Bommasani, Creel, Bana, Jurafsky & Liang, “Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes,” NeurIPS 2023, arXiv:2307.05862 | source_ids: SRC-SL-TOUPS-ET-AL-NEURIPS-2023 |
| 7 | SOURCE TYPE | Peer-reviewed conference paper | classes: PEER_REVIEWED_CONFERENCE_PAPER |
| 8 | INSTITUTIONAL POSITION | Independent academic (Stanford University-led) | — |
| 9 | SCOPE | Commercial classification services (sentiment, speech and images) and a dermatology dataset | — |
| 10 | DESIGN | Ecosystem-level comparison of observed shared failure with an independence baseline | — |
| 11 | SAMPLE / DATASET | 11 datasets and 9 commercial services over three years; 656 dermatology images rated by models and two dermatologists | — |
| 12 | MODEL / TOOL / VERSION | Commercial machine-learning APIs of several vendors, and research models in the dermatology case | — |
| 13 | COMPARATOR | An independence baseline; human dermatologists | — |
| 14 | MAIN RESULT | Systemic failure above baseline in every dataset; 10% of a system’s improvements on systemic failures against 27% of improvable cases; human outcomes more homogeneous than model outcomes in dermatology, though models were more homogeneous on darker skin | — |
| 15 | INFERENTIAL TARGET | Whether individual system improvements reach the people every system fails | — |
| 16 | DOES NOT LICENSE | Findings about generative language models | — |
| 17 | VERIFICATION STATE | CONTENT VERIFIED | state: CONTENT_VERIFIED_PRIMARY · verified_by: UNKNOWN |
| 18 | LIMITATIONS | Classification systems, not generative models; the excess depends on the independence baseline (see LD09-SL-C26); two human raters; read in the arXiv version 2 of the NeurIPS 2023 paper; the proceedings version was not read | — |
| 19 | CONTRADICTORY / SUPPORTING SOURCES | Baseline dependence: LD09-SL-C26 | — |
| 20 | GEOGRAPHY / LANGUAGE | Not checked / English | — |
| 21 | DISTRIBUTIONAL LEDGER | Models were more homogeneous on images of darker skin, a disparity humans did not show | — |
| 22 | R01 / FIELD-MAP LINKS | LD-09, D3 | — |
| 23 | WHAT WOULD CHANGE THIS CLAIM? | Analyses with difficulty-adjusted baselines, and studies of generative systems | — |
| 24 | PUBLICATION STATUS | ELIGIBLE WITH QUALIFIER | state: ELIGIBLE · qualifiers: QUALIFIED |
| 25 | RECORDED DATE | 2026-10-15 | — |
Field 22 references resolved against Field Map v0.2: LD-09 (LIVING_DEBATE); D3 (DOMAIN)
Cited by Living Debate versions
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 5a2da8de49773cd0cd032703103974c472088934f4fe4017489a577ad100114d.
Repository-authored inputs (not Drive exports)
- SocioLens claim — LD09-SL-C23 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 23 — LD09-SL-C23” SHA-256
e460f1737edef2aafae6b8a9398b1ccf9442c7ca46de4b7d3dcd83e40fbe04ab; fixturefixtures/authored/claims/LD09-SL-C23--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-C23 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-C23 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-TOUPS-ET-AL-NEURIPS-2023--v1.0 — basis: field 6 of ENTRY 23 — LD09-SL-C23 cites "Toups, Bommasani, Creel, Bana, Jurafsky & Liang, “Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes,” NeurIPS 2023, arXiv:2307.05862"; the only field-6 segment of this ClaimVersion's repository-authored ledger block.
- SOURCE_PROVENANCE_PIN = SRC-SL-TOUPS-ET-AL-NEURIPS-2023--v1.0 — basis: explicit version-pinned public provenance of this ClaimVersion (Amendment v1.2 §6): the pinned field-6 Source record "Toups, Bommasani, Creel, Bana, Jurafsky & Liang, “Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes,” NeurIPS 2023, arXiv:2307.05862".