{
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  "version": "1.1.0",
  "last_updated": "2026-06-06",
  "description": "Source-of-truth for the PAICE portfolio papers index. Hand-edited. Build script scripts/build-papers.mjs reads this file and writes papers/index.html (human surface) and api/v1/papers.json (machine surface). Do not edit those outputs directly.",
  "papers": [
    {
      "id": "aggregated-intelligence",
      "title": "Aggregated Intelligence",
      "subtitle": "The case for measuring human-machine collaboration independently while it can still be seen",
      "type": "executive-brief",
      "publisher": "PAICE.work PBC",
      "steward": "paice-foundation",
      "author": "Sam Rogers",
      "author_role": "Founder of PAICE.work PBC",
      "version": "v1",
      "published_date": "2026-06",
      "last_updated": "2026-06-06",
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      "pdf_url": "https://paice.foundation/papers/aggregated-intelligence.pdf",
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      "audio_url": "https://paice.foundation/papers/aggregated-intelligence.m4a",
      "video_url": "https://youtu.be/747A7CxAxDg",
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      "og_image": "https://paice.foundation/imgs/aggregated-intelligence-og.jpg",
      "companion_paper_id": "one-number-you-can-defend",
      "companion_label": "Companion to \"One Number You Can Defend\"",
      "abstract": "The intelligence that determines an organization's outcomes is not its people alone, and not its models alone. It is the two working together, well or badly. Aggregated Intelligence is the collective output of different intelligences working together toward a clear intent. This brief argues that it can be measured, but only a measure independent of the tools it scores can be trusted, and that the window to baseline the collaboration is open now and narrowing.",
      "featured": true,
      "tags": [
        "aggregated-intelligence",
        "anti-fragility",
        "bottleneck-migration",
        "independence",
        "investor"
      ]
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    {
      "id": "one-number-you-can-defend",
      "title": "One Number You Can Defend",
      "subtitle": "The companion paper to Aggregated Intelligence; details the AI Posture instrument.",
      "type": "executive-brief",
      "publisher": "PAICE.work PBC",
      "steward": "aiposture",
      "author": "Sam Rogers",
      "version": "v1",
      "published_date": "2026-06",
      "last_updated": "2026-06-06",
      "canonical_url": "https://aiposture.org/papers/",
      "abstract": "Details the AI Posture instrument: one governance score, bounded by the weakest vector, for governance, risk, and compliance leaders. Companion to Aggregated Intelligence.",
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      "tags": [
        "ai-posture",
        "grc",
        "compliance",
        "weakest-link",
        "governance-score"
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    {
      "id": "making-ai-collaboration-measurable",
      "title": "PAICE.work: Making AI Collaboration Measurable, Teachable, and Governable",
      "subtitle": "A framework for assessing collaboration capability, governance readiness, and risk in AI-assisted systems",
      "document_type_label": "Vision & Partnership Whitepaper",
      "type": "whitepaper",
      "publisher": "PAICE.work PBC",
      "author": "Sam Rogers",
      "author_role": "Founder of PAICE.work PBC",
      "steward": "paice-work",
      "version": "v4",
      "published_date": "2025-11-12",
      "last_updated": "2025-12-15",
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      "pdf_url": "https://paice.work/papers/making-ai-collaboration-measurable.pdf",
      "presented_at": "Originally presented at DevLearn 2025, Las Vegas",
      "abstract": "Rationale, framework, and architecture behind PAICE: the evidence layer for AI governance, measuring observable behavior rather than self-report.",
      "featured": false,
      "tags": [
        "behavioral-measurement",
        "framework",
        "ai-governance"
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    {
      "id": "closing-the-collaboration-gap",
      "title": "Closing the Collaboration Gap",
      "subtitle": "A Behavioral Skill Framework for Human-AI Performance Improvement",
      "type": "whitepaper",
      "publisher": "PAICE.work PBC",
      "author": "Sam Rogers",
      "author_role": "Founder of PAICE.work PBC",
      "steward": "paice-work",
      "version": "v1",
      "published_date": "2026-03-31",
      "last_updated": "2026-03-31",
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      "pdf_url": "https://paice.work/papers/closing-the-collaboration-gap.pdf",
      "presented_at": "2026 ISPI Performance Improvement Conference, Nashville, Tennessee",
      "abstract": "Maps People+AI collaboration measurement to established performance-improvement frameworks from Gilbert, Mager, Rummler-Brache, Thalheimer, Phillips, and Brinkerhoff.",
      "featured": false,
      "tags": [
        "ispi",
        "human-performance-technology",
        "collaboration",
        "behavioral-skill"
      ]
    },
    {
      "id": "verifiable-human-ai-collaboration",
      "title": "Verifiable Human-AI Collaboration",
      "subtitle": "Privacy-Preserving Assessment with Cryptographic Integrity",
      "tagline": "Behavioral Observation, TEE-Protected Inference, and On-Chain Attestation",
      "type": "whitepaper",
      "publisher": "PAICE.work PBC",
      "author": "Sam Rogers",
      "author_role": "Founder of PAICE.work PBC",
      "steward": "paice-work",
      "version": "v1",
      "published_date": "2026-02-24",
      "last_updated": "2026-02-24",
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      "pdf_url": "https://paice.work/papers/verifiable-human-ai-collaboration.pdf",
      "presented_at": "NEARCON 2026, San Francisco",
      "abstract": "Verifies human-AI collaboration without exposing the collaboration itself. Behavioral observation routed through Trusted Execution Environments on NEAR AI Cloud; assessment results committed as tamper-proof on-chain attestations. Provable privacy with verifiable integrity, in regulated industries where the observation data is high-stakes.",
      "featured": false,
      "tags": [
        "privacy",
        "security",
        "tee",
        "cryptographic-attestation",
        "near",
        "verifiable"
      ]
    },
    {
      "id": "cost-of-invisible-ai-risk",
      "title": "The Cost of Invisible AI Risk",
      "subtitle": "A Board-Level Business Case for Measuring AI-Collaboration Reliability",
      "type": "whitepaper",
      "publisher": "PAICE.work PBC",
      "author": "Sam Rogers",
      "author_role": "Founder of PAICE.work PBC",
      "steward": "paice-work",
      "version": "v1.0",
      "published_date": "2026-06-01",
      "last_updated": "2026-06-01",
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      "pdf_url": "https://paice.work/papers/cost-of-invisible-ai-risk.pdf",
      "abstract": "A board-level business case for measuring AI-collaboration reliability: quantifies the operational exposure created by ungoverned human-AI workflows and the cost of running without behavioral evidence.",
      "featured": false,
      "tags": [
        "board",
        "business-case",
        "operational-risk",
        "behavioral-reliability"
      ]
    },
    {
      "id": "governance-without-surveillance",
      "title": "Governance Without Surveillance",
      "subtitle": "Why Privacy by Architecture Is What Makes Behavioral AI Measurement Adoptable",
      "type": "whitepaper",
      "publisher": "PAICE.work PBC",
      "author": "Sam Rogers",
      "author_role": "Founder of PAICE.work PBC",
      "steward": "paice-work",
      "version": "v1.0",
      "published_date": "2026-05-01",
      "last_updated": "2026-05-01",
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      "pdf_url": "https://paice.work/papers/governance-without-surveillance.pdf",
      "abstract": "Privacy by architecture is the precondition for behavioral AI measurement to be adoptable inside regulated organizations. Behavioral evidence without surveillance: what the design has to refuse to capture, and why that refusal is what unlocks the assessment.",
      "featured": false,
      "tags": [
        "privacy-by-architecture",
        "governance",
        "behavioral-measurement",
        "adoptability"
      ]
    },
    {
      "id": "people-vector-evidence-layer",
      "title": "The People-Vector Evidence Layer for AI Governance Frameworks",
      "subtitle": "Mapping Behavioral Assessment to NIST AI RMF, ISO/IEC 42001, and the EU AI Act",
      "type": "whitepaper",
      "publisher": "PAICE.work PBC",
      "author": "Sam Rogers",
      "author_role": "Founder of PAICE.work PBC",
      "steward": "paice-work",
      "version": "v1.1",
      "published_date": "2026-06-01",
      "last_updated": "2026-06-01",
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      "pdf_url": "https://paice.work/papers/people-vector-evidence-layer.pdf",
      "abstract": "Maps PAICE behavioral assessment to the people-vector requirements inside NIST AI RMF, ISO/IEC 42001, and the EU AI Act. Shows what each framework asks for on the human side of AI-assisted work, and what behavioral evidence satisfies it.",
      "featured": false,
      "tags": [
        "nist-ai-rmf",
        "iso-42001",
        "eu-ai-act",
        "compliance-mapping",
        "people-vector"
      ]
    }
  ]
}