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  <title>SocioLens — LD-06: The evaluation apparatus: Can benchmarks, leaderboards and audits carry the weight placed on them?</title>
  <subtitle>New versions of this debate, revisions of its claims, Radar updates and research opportunities.</subtitle>
  <link rel="self" type="application/atom+xml" href="https://sociolens-web.pages.dev/debates/ld-06/feed.xml"/>
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  <updated>2026-10-16T00:00:00Z</updated>
  <author><name>SocioLens</name></author>
  <entry>
    <id>urn:uuid:4e856f02-3fb2-5ec5-b124-4dcc8467c485</id>
    <title type="text">New research opportunity: RO-LD12-07 — What do evaluators, researchers and regulators actually get to see under EU law?</title>
    <link rel="alternate" type="text/html" href="https://sociolens-web.pages.dev/opportunities/ro-ld12-07/v1/"/>
    <updated>2026-10-16T00:00:00Z</updated>
    <summary type="text">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&apos;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?</summary>
  </entry>
  <entry>
    <id>urn:uuid:3f3272d8-ee14-5fb2-82b2-fe8a79e49f1a</id>
    <title type="text">New debate: LD-06 v0.1 — The evaluation apparatus: Can benchmarks, leaderboards and audits carry the weight placed on them?</title>
    <link rel="alternate" type="text/html" href="https://sociolens-web.pages.dev/debates/ld-06/v0.1/"/>
    <updated>2026-10-15T00:00:00Z</updated>
    <summary type="text">Benchmark scores, leaderboard ranks, model judges, developers&apos; own reports, red-teaming and audits are now used to decide what research is published, which models are bought, which are released and which comply with the law. Can they carry that weight?</summary>
  </entry>
  <entry>
    <id>urn:uuid:18ed36e2-8cb2-5459-bed4-4747c265f9de</id>
    <title type="text">New research opportunity: RO-LD06-01 — Do benchmark scores predict what models achieve in real deployments?</title>
    <link rel="alternate" type="text/html" href="https://sociolens-web.pages.dev/opportunities/ro-ld06-01/v1/"/>
    <updated>2026-10-15T00:00:00Z</updated>
    <summary type="text">Across many language models, do scores on public benchmarks predict measured outcomes when the same models are used in real work, such as code that maintainers merge in live repositories, support tickets resolved or error rates in use, and are the benchmarks calibrated to those outcomes or only correct in rank?</summary>
  </entry>
  <entry>
    <id>urn:uuid:aac14651-7204-5c1c-883d-a31e94a87f1b</id>
    <title type="text">New research opportunity: RO-LD06-02 — How faithfully do indices and policy documents relay AI evaluation results?</title>
    <link rel="alternate" type="text/html" href="https://sociolens-web.pages.dev/opportunities/ro-ld06-02/v1/"/>
    <updated>2026-10-15T00:00:00Z</updated>
    <summary type="text">When annual indices, industry reports and government or procurement documents cite benchmark and evaluation figures, what share come from the developers themselves, what share are independently measured, and how often is the figure, its unit or its scope changed between the primary source and the relay?</summary>
  </entry>
  <entry>
    <id>urn:uuid:3d4de4fc-0194-5eeb-b382-e01770eb1416</id>
    <title type="text">New research opportunity: RO-LD06-03 — How much does knowing it is tested change a model&apos;s measured safety?</title>
    <link rel="alternate" type="text/html" href="https://sociolens-web.pages.dev/opportunities/ro-ld06-03/v1/"/>
    <updated>2026-10-15T00:00:00Z</updated>
    <summary type="text">When the same safety scenarios are presented with and without minimal cues that they are tests, how large is the gap in measured behaviour across open and closed frontier models, and does suppressing evaluation awareness in open-weight models on the same items reproduce the gap?</summary>
  </entry>
  <entry>
    <id>urn:uuid:842eabc6-a517-59a2-86c6-f8bbdfe99993</id>
    <title type="text">New research opportunity: RO-LD06-04 — How is external evaluation under the EU AI Code of Practice carried out?</title>
    <link rel="alternate" type="text/html" href="https://sociolens-web.pages.dev/opportunities/ro-ld06-04/v1/"/>
    <updated>2026-10-15T00:00:00Z</updated>
    <summary type="text">Under the EU Code of Practice for general-purpose AI models with systemic risk, which signatories appoint independent external evaluators, how often are the exceptions used (a model judged similarly safe or safer, or no evaluator found after a public call), and what access and time do evaluators actually receive?</summary>
  </entry>
  <entry>
    <id>urn:uuid:fa107003-8e4e-534c-99b5-c8be70273fc9</id>
    <title type="text">New research opportunity: RO-LD06-05 — Who builds and checks China&apos;s compliance test banks for generative AI?</title>
    <link rel="alternate" type="text/html" href="https://sociolens-web.pages.dev/opportunities/ro-ld06-05/v1/"/>
    <updated>2026-10-15T00:00:00Z</updated>
    <summary type="text">Under China&apos;s national standard GB/T 45654-2025, who builds the generated-content and refusal test banks that decide whether a generative AI service passes, how are the banks validated, and do providers&apos; banks and third-party evaluators&apos; banks give the same verdicts on the same models?</summary>
  </entry>
  <entry>
    <id>urn:uuid:e071e332-1598-553f-b0d0-93d5186e5ed9</id>
    <title type="text">New research opportunity: RO-LD06-06 — How many published AI evaluations outside medicine can no longer be repeated?</title>
    <link rel="alternate" type="text/html" href="https://sociolens-web.pages.dev/opportunities/ro-ld06-06/v1/"/>
    <updated>2026-10-15T00:00:00Z</updated>
    <summary type="text">Among published studies in NLP, the social sciences and education research that evaluate closed language models through an API, what share used a model version that has since been retired or is due to retire, and how many of a sample can still be re-run to reproduce their main result?</summary>
  </entry>
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