urn:uuid:9e1c2947-4f42-5756-b718-a9b1bf71de2a SocioLens — LD-08: Who can know about AI? Epistemic dependence and jurisdiction over the field New versions of this debate, revisions of its claims, Radar updates and research opportunities. 2026-10-16T00:00:00Z SocioLens urn:uuid:a116288a-d6c6-560f-8dd0-4f8e7c7fd45e New debate: LD-08 v0.1 — Who can know about AI? Epistemic dependence and jurisdiction over the field 2026-10-16T00:00:00Z AI firms build the largest models, hold the compute, collect the records of how people use AI, fund research on its effects and decide which outside researchers may test their systems. How independent can research on AI's social effects be under these conditions, and who is gaining the authority to say what AI does to society? urn:uuid:2345e580-867d-5dc0-98d4-394a5c6e102a New research opportunity: RO-LD08-01 — Does industry funding or data access change what research on AI's social effects finds? 2026-10-16T00:00:00Z 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? urn:uuid:81b23924-914a-557f-8c32-74898ea59112 New research opportunity: RO-LD08-02 — How tied to industry are researchers of AI's social effects outside computer science? 2026-10-16T00:00:00Z 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? urn:uuid:9a7e0e4c-6c13-5e4e-be78-fec6606df838 New research opportunity: RO-LD08-03 — Do developers' usage statistics hold up when someone else classifies the conversations? 2026-10-16T00:00:00Z When the same chatbot conversations are classified independently of the developer, with a validated scheme and human-coded benchmarks, how far do headline usage measures such as the automation share, the work share and task success move away from the figures developers publish? urn:uuid:84a3b190-50ea-539d-b650-adcda5dc7638 New research opportunity: RO-LD08-04 — Does the EU's researcher-access law actually reach AI features, and who gets in? 2026-10-16T00:00:00Z Under Article 40 of the Digital Services Act, which vetted-researcher applications concerning AI features of very large platforms are granted, refused or delayed, on what grounds, and how long does each stage take? urn:uuid:cc4b321b-9184-5e14-a7f8-d1100821b8dd New research opportunity: RO-LD08-05 — Who writes research on AI's social effects outside arXiv? 2026-10-16T00:00:00Z Across social-science and humanities journals, books, policy reports and arXiv, what share of research on AI's social effects comes from computer-science teams, social scientists, mixed teams, industry and government, and how has that share changed since 2014? urn:uuid:5dc82c3a-470d-5e71-aa6d-8377ff14b097 New research opportunity: RO-LD08-06 — Whose conversations do data donations capture? 2026-10-16T00:00:00Z How do people who donate their chatbot conversations to researchers differ from the developers' user bases, and how much do findings from donated data change once donors are weighted to the user population? urn:uuid:4e856f02-3fb2-5ec5-b124-4dcc8467c485 New research opportunity: RO-LD12-07 — What do evaluators, researchers and regulators actually get to see under EU law? 2026-10-16T00:00:00Z For general-purpose AI providers that also run platforms or search engines designated under the Digital Services Act, what does each legal route for outside scrutiny deliver in practice (external evaluation under the GPAI Code of Practice, which applies only to models with systemic risk, vetted-researcher data access under the Digital Services Act, and the AI Office's information requests and model evaluations), recorded for each route as what object is reached (model, platform data or documents), who can start it, how long it takes and how deep the access goes? urn:uuid:d3f7d835-fdba-5de5-91d0-f9667ff8f8e7 Claim LD02-HQ-C02 revised to v1.1 2026-10-14T00:00:00Z What changed: The distributional ledger no longer says that an early-career earnings and hours null is reported. It now gives what the March 2026 revision of the paper reports for early-career workers (aged 22–25): their employment fell in several of the occupations, but their share of employment did not change differentially at workplaces that encourage chatbot use, with effects above about a third of a percentage point ruled out. The contradictory/supporting field now also notes the paper's workplace-level nulls for employment (total hours), job churn and the early-career share, and that Danmarks Nationalbank Working Paper 223 finds lower employment growth at AI-adopting firms. The public-facing claim, the 2% bound, the 11 occupations and the sample of about 25,000 workers in 7,000 workplaces are unchanged. Why: A recheck on 14 October 2026 read the full text of NBER Working Paper 33777 as revised in March 2026 (retitled "Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI"; previously "Large Language Models, Small Labor Market Effects"). Version 1.0 was built from the paper's abstract, and the HQ batch notes left "full Humlum/Vestergaard early-career ... details" open. The full text reports early-career results only as the employment share at adopting workplaces (Section 3.4, footnote 21), not as earnings or hours, and the same section reports no differential change in workplace employment (total hours) or job churn. The main result is confirmed in the full text: confidence intervals rule out average earnings and hours effects larger than 2% two years after ChatGPT, in 11 occupations, with 25,000 workers across 7,000 workplaces in the late-2024 survey round. The recheck and this revision were done by an AI agent reading the paper, not by a person.