48 lines
3.3 KiB
Markdown
48 lines
3.3 KiB
Markdown
---
|
|
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: unprocessed
|
|
priority: medium
|
|
tags: [collective-intelligence, national-scale, AI-infrastructure, federated-learning, diversity, trust]
|
|
flagged_for_vida: ["healthcare applications of AI-enhanced collective intelligence"]
|
|
---
|
|
|
|
## 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
|