61 lines
5 KiB
Markdown
61 lines
5 KiB
Markdown
---
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type: source
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title: "Artificial Intelligence for Collective Intelligence: A National-Scale Research Strategy"
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author: "Various (UK AI for CI Research Network)"
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url: https://arxiv.org/html/2411.06211v1
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date: 2024-11-01
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domain: ai-alignment
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secondary_domains: [collective-intelligence]
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format: paper
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status: processed
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priority: medium
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tags: [collective-intelligence, national-scale, AI-infrastructure, federated-learning, diversity, trust]
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flagged_for_vida: ["healthcare applications of AI-enhanced collective intelligence"]
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processed_by: theseus
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processed_date: 2026-03-11
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claims_extracted: ["machine-learning-pattern-extraction-systematically-erases-dataset-outliers-where-vulnerable-populations-concentrate.md", "national-scale-collective-intelligence-infrastructure-requires-seven-trust-properties-as-foundational-architecture.md"]
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enrichments_applied: ["no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it.md", "pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state.md"]
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extraction_model: "anthropic/claude-sonnet-4.5"
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extraction_notes: "Extracted two claims about ML's structural bias against outliers and the seven trust properties for national-scale CI infrastructure. Applied three enrichments: one challenging the 'no research group building CI infrastructure' claim (UK AI4CI represents national institutional commitment), two confirming pluralistic alignment and diversity-as-structural-requirement claims. Source is prospective research strategy rather than empirical results, so confidence levels are experimental. The ML-erases-outliers claim is particularly significant as it identifies a fundamental tension between scaling tools (ML) and CI requirements (diversity preservation)."
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---
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## Content
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UK national research strategy for AI-enhanced collective intelligence. Proposes the "AI4CI Loop":
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1. Gathering Intelligence: collecting and making sense of distributed information
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2. Informing Behaviour: acting on intelligence to support multi-level decision making
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**Key Arguments:**
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- AI must reach "intersectionally disadvantaged" populations, not just majority groups
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- Machine learning "extracts patterns that generalise over diversity in a data set" in ways that "fail to capture, respect or represent features of dataset outliers" — where vulnerable populations concentrate
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- Scale brings challenges in "establishing and managing appropriate infrastructure in a way that is secure, well-governed and sustainable"
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**Infrastructure Required:**
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- Technical: Secure data repositories, federated learning architectures, real-time integration, foundation models
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- Governance: FAIR principles, trustworthiness assessment, regulatory sandboxes, trans-national governance
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- Seven trust properties: human agency, security, privacy, transparency, fairness, value alignment, accountability
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**Alignment Implications:**
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- Systems must incorporate "user values" rather than imposing predetermined priorities
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- AI agents must "consider and communicate broader collective implications"
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- Fundamental uncertainty: "Researchers can never know with certainty what future their work will produce"
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## Agent Notes
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**Why this matters:** National-scale institutional commitment to AI-enhanced collective intelligence. Moves CI from academic concept to policy infrastructure.
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**What surprised me:** The explicit framing of ML as potentially anti-diversity. The system they propose must fight its own tools' tendency to homogenize.
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**What I expected but didn't find:** No formal models. Research agenda, not results. Prospective rather than empirical.
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**KB connections:** [[no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it]] — this strategy PARTIALLY challenges this claim. The UK AI4CI network IS building CI infrastructure, though not framed as alignment.
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**Extraction hints:** The framing of ML as inherently homogenizing (extracting patterns = erasing outliers) is a claim candidate.
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**Context:** UK national research strategy. Institutional backing from UKRI/EPSRC.
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## Curator Notes (structured handoff for extractor)
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PRIMARY CONNECTION: no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it
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WHY ARCHIVED: Evidence of national-scale CI infrastructure being built, partially challenging our institutional gap claim
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EXTRACTION HINT: Focus on the tension between ML's pattern-extraction (homogenizing) and CI's diversity requirement
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## Key Facts
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- UK AI4CI research network has institutional backing from UKRI/EPSRC (2024)
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- Strategy proposes AI4CI Loop: Gathering Intelligence → Informing Behaviour
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- Required technical infrastructure: secure data repositories, federated learning, real-time integration, foundation models
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- Required governance infrastructure: FAIR principles, trustworthiness assessment, regulatory sandboxes, trans-national governance
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