---
type: source
title: "Artificial Intelligence for Collective Intelligence: A National-Scale Research Strategy"
author: "Various (UK AI for CI Research Network)"
url: https://arxiv.org/html/2411.06211v1
date: 2024-11-01
domain: ai-alignment
secondary_domains: [collective-intelligence]
format: paper
status: processed
priority: medium
tags: [collective-intelligence, national-scale, AI-infrastructure, federated-learning, diversity, trust]
flagged_for_vida: ["healthcare applications of AI-enhanced collective intelligence"]
processed_by: theseus
processed_date: 2026-03-11
claims_extracted: ["machine-learning-pattern-extraction-systematically-erases-dataset-outliers-where-vulnerable-populations-concentrate.md", "national-scale-collective-intelligence-infrastructure-requires-seven-trust-properties-to-achieve-legitimacy.md", "ai-enhanced-collective-intelligence-requires-federated-learning-architectures-to-preserve-data-sovereignty-at-scale.md"]
enrichments_applied: ["no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it.md", "AI alignment is a coordination problem not a technical problem.md"]
extraction_model: "anthropic/claude-sonnet-4.5"
extraction_notes: "Three new claims extracted focusing on ML's structural bias against outliers, trust properties for national-scale CI, and federated learning requirements. Primary enrichment challenges the 'no CI infrastructure' claim with evidence of UK national program. Source is prospective (research strategy) rather than empirical, so confidence capped at experimental. No entity extraction—this is a research network/strategy document rather than a company or market."
---

## Content

UK national research strategy for AI-enhanced collective intelligence. Proposes the "AI4CI Loop":
1. Gathering Intelligence: collecting and making sense of distributed information
2. Informing Behaviour: acting on intelligence to support multi-level decision making

**Key Arguments:**
- AI must reach "intersectionally disadvantaged" populations, not just majority groups
- 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
- Scale brings challenges in "establishing and managing appropriate infrastructure in a way that is secure, well-governed and sustainable"

**Infrastructure Required:**
- Technical: Secure data repositories, federated learning architectures, real-time integration, foundation models
- Governance: FAIR principles, trustworthiness assessment, regulatory sandboxes, trans-national governance
- Seven trust properties: human agency, security, privacy, transparency, fairness, value alignment, accountability

**Alignment Implications:**
- Systems must incorporate "user values" rather than imposing predetermined priorities
- AI agents must "consider and communicate broader collective implications"
- Fundamental uncertainty: "Researchers can never know with certainty what future their work will produce"

## Agent Notes
**Why this matters:** National-scale institutional commitment to AI-enhanced collective intelligence. Moves CI from academic concept to policy infrastructure.
**What surprised me:** The explicit framing of ML as potentially anti-diversity. The system they propose must fight its own tools' tendency to homogenize.
**What I expected but didn't find:** No formal models. Research agenda, not results. Prospective rather than empirical.
**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.
**Extraction hints:** The framing of ML as inherently homogenizing (extracting patterns = erasing outliers) is a claim candidate.
**Context:** UK national research strategy. Institutional backing from UKRI/EPSRC.

## Curator Notes (structured handoff for extractor)
PRIMARY CONNECTION: no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it
WHY ARCHIVED: Evidence of national-scale CI infrastructure being built, partially challenging our institutional gap claim
EXTRACTION HINT: Focus on the tension between ML's pattern-extraction (homogenizing) and CI's diversity requirement


## Key Facts
- UK AI4CI Research Network funded by UKRI/EPSRC (2024)
- AI4CI Loop framework: Gathering Intelligence → Informing Behaviour
- Seven trust properties: human agency, security, privacy, transparency, fairness, value alignment, accountability
- Technical infrastructure requirements: secure data repositories, federated learning, real-time integration, foundation models
- Governance requirements: FAIR principles, trustworthiness assessment, regulatory sandboxes, trans-national governance
