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8 changed files with 2 additions and 101 deletions
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@ -29,12 +29,6 @@ This evidence directly challenges the theory that governance pressure (declarati
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The alignment implication: transparency is a prerequisite for external oversight. If [[pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations]], declining transparency makes even the unreliable evaluations harder to conduct. The governance mechanisms that could provide oversight (safety institutes, third-party auditors) depend on lab cooperation that is actively eroding.
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### Additional Evidence (extend)
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*Source: [[2024-12-00-uuk-mitigations-gpai-systemic-risks-76-experts]] | Added: 2026-03-19*
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Expert consensus identifies 'external scrutiny, proactive evaluation and transparency' as the key principles for mitigating AI systemic risks, with third-party audits as the top-3 implementation priority. The transparency decline documented by Stanford FMTI is moving in the opposite direction from what 76 cross-domain experts identify as necessary.
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---
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Relevant Notes:
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@ -42,12 +42,6 @@ This pattern confirms [[voluntary safety pledges cannot survive competitive pres
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The EU AI Act's enforcement mechanisms (penalties up to €35 million or 7% of global turnover) and US state-level rules taking effect across 2026 represent the shift from voluntary commitments to binding regulation. The article frames 2026 as the year regulatory frameworks collide with actual deployment at scale, confirming that enforcement, not voluntary pledges, is the governance mechanism with teeth.
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### Additional Evidence (confirm)
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*Source: [[2024-12-00-uuk-mitigations-gpai-systemic-risks-76-experts]] | Added: 2026-03-19*
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Third-party pre-deployment audits are the top expert consensus priority (>60% agreement across AI safety, CBRN, critical infrastructure, democratic processes, and discrimination domains), yet no major lab implements them. This is the strongest available evidence that voluntary commitments cannot deliver what safety requires—the entire expert community agrees on the priority, and it still doesn't happen.
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---
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Relevant Notes:
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@ -5,12 +5,6 @@ domain: ai-alignment
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created: 2026-03-11
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confidence: likely
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source: "AI Safety Grant Application (LivingIP)"
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### Additional Evidence (extend)
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*Source: [[2024-12-00-uuk-mitigations-gpai-systemic-risks-76-experts]] | Added: 2026-03-19*
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Expert consensus from 76 specialists across 5 risk domains defines what 'building alignment mechanisms' should include: third-party pre-deployment audits, safety incident reporting with information sharing, and pre-deployment risk assessments are the top-3 priorities with >60% cross-domain agreement. The convergence of biosecurity experts, AI safety researchers, critical infrastructure specialists, democracy defenders, and discrimination researchers on the same top-3 list provides empirical specification of which mechanisms matter most.
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---
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# safe AI development requires building alignment mechanisms before scaling capability
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@ -33,12 +33,6 @@ Anthropic, widely considered the most safety-focused frontier AI lab, rolled bac
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The International AI Safety Report 2026 (multi-government committee, February 2026) confirms that risk management remains 'largely voluntary' as of early 2026. While 12 companies published Frontier AI Safety Frameworks in 2025, these remain voluntary commitments without binding legal requirements. The report notes that 'a small number of regulatory regimes beginning to formalize risk management as legal requirements,' but the dominant governance mode is still voluntary pledges. This provides multi-government institutional confirmation that the structural race-to-the-bottom predicted by the alignment tax is actually occurring—voluntary frameworks are not transitioning to binding requirements at the pace needed to prevent competitive pressure from eroding safety commitments.
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### Additional Evidence (confirm)
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*Source: [[2024-12-00-uuk-mitigations-gpai-systemic-risks-76-experts]] | Added: 2026-03-19*
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The gap between expert consensus (76 specialists identify third-party audits as top-3 priority) and actual implementation (no mandatory audit requirements at major labs) demonstrates that knowing what's needed is insufficient. Even when the field's experts across multiple domains agree on priorities, competitive dynamics prevent voluntary adoption.
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---
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Relevant Notes:
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@ -1,24 +0,0 @@
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{
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"rejected_claims": [
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{
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"filename": "expert-consensus-identifies-third-party-audits-as-top-priority-but-no-mandatory-implementation-exists.md",
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"issues": [
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"missing_attribution_extractor"
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]
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}
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],
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"validation_stats": {
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"total": 1,
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"kept": 0,
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"fixed": 1,
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"rejected": 1,
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"fixes_applied": [
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"expert-consensus-identifies-third-party-audits-as-top-priority-but-no-mandatory-implementation-exists.md:set_created:2026-03-19"
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],
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"rejections": [
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"expert-consensus-identifies-third-party-audits-as-top-priority-but-no-mandatory-implementation-exists.md:missing_attribution_extractor"
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]
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},
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"model": "anthropic/claude-sonnet-4.5",
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"date": "2026-03-19"
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}
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@ -1,27 +0,0 @@
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{
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"rejected_claims": [
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{
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"filename": "privacy-enhancing-technologies-enable-independent-ai-scrutiny-without-ip-compromise-but-legal-authority-to-require-scrutiny-does-not-exist.md",
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"issues": [
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"missing_attribution_extractor"
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]
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}
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],
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"validation_stats": {
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"total": 1,
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"kept": 0,
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"fixed": 4,
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"rejected": 1,
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"fixes_applied": [
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"privacy-enhancing-technologies-enable-independent-ai-scrutiny-without-ip-compromise-but-legal-authority-to-require-scrutiny-does-not-exist.md:set_created:2026-03-19",
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"privacy-enhancing-technologies-enable-independent-ai-scrutiny-without-ip-compromise-but-legal-authority-to-require-scrutiny-does-not-exist.md:stripped_wiki_link:voluntary-safety-pledges-cannot-survive-competitive-pressure",
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"privacy-enhancing-technologies-enable-independent-ai-scrutiny-without-ip-compromise-but-legal-authority-to-require-scrutiny-does-not-exist.md:stripped_wiki_link:only-binding-regulation-with-enforcement-teeth-changes-front",
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"privacy-enhancing-technologies-enable-independent-ai-scrutiny-without-ip-compromise-but-legal-authority-to-require-scrutiny-does-not-exist.md:stripped_wiki_link:safe-AI-development-requires-building-alignment-mechanisms-b"
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],
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"rejections": [
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"privacy-enhancing-technologies-enable-independent-ai-scrutiny-without-ip-compromise-but-legal-authority-to-require-scrutiny-does-not-exist.md:missing_attribution_extractor"
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]
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},
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"model": "anthropic/claude-sonnet-4.5",
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"date": "2026-03-19"
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}
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@ -7,13 +7,9 @@ date: 2024-12-01
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domain: ai-alignment
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secondary_domains: []
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format: paper
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status: enrichment
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status: unprocessed
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priority: high
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tags: [evaluation-infrastructure, third-party-audit, expert-consensus, systemic-risk, mitigation-prioritization]
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processed_by: theseus
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processed_date: 2026-03-19
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enrichments_applied: ["safe AI development requires building alignment mechanisms before scaling capability.md", "voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints.md", "only binding regulation with enforcement teeth changes frontier AI lab behavior because every voluntary commitment has been eroded abandoned or made conditional on competitor behavior when commercially inconvenient.md", "AI transparency is declining not improving because Stanford FMTI scores dropped 17 points in one year while frontier labs dissolved safety teams and removed safety language from mission statements.md"]
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extraction_model: "anthropic/claude-sonnet-4.5"
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---
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## Content
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@ -55,11 +51,3 @@ PRIMARY CONNECTION: [[safe AI development requires building alignment mechanisms
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WHY ARCHIVED: Provides expert consensus evidence for the evaluation infrastructure gap. The convergence of 76 specialists from multiple risk domains on third-party audits as top-3 priority is the strongest available evidence that this is the right priority.
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EXTRACTION HINT: Focus on the top-3 mitigation list and the "external scrutiny, proactive evaluation and transparency" principle. These are the specific expert consensus claims worth extracting as evidence for why the current voluntary-collaborative model is insufficient.
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## Key Facts
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- Survey included 76 specialists across AI safety, critical infrastructure, democratic processes, CBRN risks, and discrimination/bias domains
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- 27 mitigation measures were evaluated through literature review
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- Top-3 mitigations had >60% agreement across all risk domains
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- Top-3 mitigations appeared in >40% of experts' preferred combinations
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- Paper is 78 pages and published December 2024
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@ -7,13 +7,9 @@ date: 2025-02-01
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domain: ai-alignment
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secondary_domains: []
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format: paper
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status: null-result
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status: unprocessed
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priority: high
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tags: [evaluation-infrastructure, privacy-enhancing-technologies, OpenMined, external-scrutiny, Christchurch-Call, AISI, deployed]
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processed_by: theseus
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processed_date: 2026-03-19
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extraction_model: "anthropic/claude-sonnet-4.5"
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extraction_notes: "LLM returned 1 claims, 1 rejected by validator"
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---
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## Content
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@ -57,11 +53,3 @@ PRIMARY CONNECTION: [[safe AI development requires building alignment mechanisms
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WHY ARCHIVED: Provides evidence that the technical barrier to independent AI evaluation is solvable. The key insight — technology ready, legal framework missing — precisely locates the bottleneck in evaluation infrastructure development.
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EXTRACTION HINT: Focus on the technology-law gap: PET infrastructure works (two deployments), but legal authority to require frontier AI labs to submit to independent evaluation doesn't exist. This is the specific intervention point.
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## Key Facts
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- Helen Toner was Director of Strategy at CISA
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- Helen Toner is at Georgetown
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- The Christchurch Call is a voluntary initiative
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- UK AI Safety Institute has conducted frontier model evaluations using PET infrastructure
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- The paper was published February 2025
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