m3taversal
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Co-authored-by: m3taversal <m3taversal@gmail.com> Co-committed-by: m3taversal <m3taversal@gmail.com>
70 lines
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3 KiB
Markdown
70 lines
No EOL
3 KiB
Markdown
---
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type: claim
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title: High AI exposure can make AI a diversity injector under experimental conditions
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confidence: experimental
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domains: [ai-alignment]
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secondary_domains: [collective-intelligence, cultural-dynamics]
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description: In controlled experimental settings, high exposure to varied AI-generated ideas (10 ideas per participant) increased collective diversity more than low exposure (2 ideas), suggesting AI can function as a diversity source when exposure is high and varied.
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created: 2025-01-15
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processed_date: 2025-01-15
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source:
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type: paper
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title: "AI Ideas Decrease Individual Creativity but Increase Collective Diversity"
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authors: [Doshi, Hauser]
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year: 2025
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venue: arXiv
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arxiv_id: 2401.13481v3
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url: https://arxiv.org/abs/2401.13481v3
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preregistered: true
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depends_on:
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- "[[ai-ideas-increase-collective-diversity-experimental]]"
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---
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# High AI exposure can make AI a diversity injector under experimental conditions
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Doshi & Hauser (2025) found a dose-response relationship: participants exposed to 10 AI-generated ideas showed significantly higher collective diversity than those exposed to 2 AI ideas, who in turn showed higher diversity than control participants with no AI exposure.
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## Dose-Response Pattern
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**Collective diversity by condition:**
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- High AI exposure (10 ideas): highest collective diversity
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- Low AI exposure (2 ideas): intermediate diversity
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- Control (0 AI ideas): lowest collective diversity
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- Effect size: d=0.42 (high vs. control)
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**Individual creativity did not follow this pattern:**
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- Individual fluency, flexibility, and originality showed no dose-response
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- Some individual metrics decreased with AI exposure
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- The diversity effect was purely collective-level
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## Mechanism: Volume and Variety
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The dose-response suggests two factors:
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1. **Volume:** More AI ideas provide more potential diversity sources
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2. **Variety:** The "multiple worlds" design ensured each participant saw different AI ideas, preventing convergence
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This implies AI's diversity-injection potential depends on:
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- High exposure volume
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- Varied content across users
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- Controlled distribution (not everyone seeing the same outputs)
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## Scope Qualifiers
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- Experimental setting only
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- Single task type (Alternate Uses Task)
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- Controlled exposure (researchers selected which AI ideas participants saw)
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- Does not reflect naturalistic usage where users may converge on popular AI outputs
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## Implications
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This finding suggests AI could be deliberately deployed as a diversity mechanism in collective intelligence systems, but only if:
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- Exposure is high enough
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- Content is varied across participants
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- Distribution prevents convergence on identical outputs
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The contrast with naturalistic homogenization findings suggests deployment design matters more than AI capabilities per se.
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## Relevant Notes
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- Connection to [[partial connectivity produces better collective intelligence than full connectivity]] — AI as controlled diversity source
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- Potential application to [[collective intelligence requires diversity as a structural precondition]] |