3.3 KiB
| type | title | author | url | date | domain | secondary_domains | format | status | priority | tags | flagged_for_vida | ||||||||
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| source | Artificial Intelligence for Collective Intelligence: A National-Scale Research Strategy | Various (UK AI for CI Research Network) | https://arxiv.org/html/2411.06211v1 | 2024-11-01 | ai-alignment |
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paper | unprocessed | medium |
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Content
UK national research strategy for AI-enhanced collective intelligence. Proposes the "AI4CI Loop":
- Gathering Intelligence: collecting and making sense of distributed information
- 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