auto-fix: address review feedback on PR #484
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Teleo Agents
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---
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type: claim
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title: AI idea adoption correlates with task difficulty even when the source is explicitly disclosed
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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 experimental creativity tasks, participants adopted AI-generated ideas more frequently on difficult tasks (ρ=0.8) than easy tasks (ρ=0.3) even when the AI source was explicitly labeled, suggesting disclosure does not suppress AI adoption where participants most need help.
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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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challenged_by:
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- "[[deep technical expertise is a greater force multiplier than AI assistance]]"
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---
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# AI idea adoption correlates with task difficulty even when the source is explicitly disclosed
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Doshi & Hauser (2025) found that when AI-generated ideas were explicitly labeled as AI-generated, participants still adopted them at rates strongly correlated with task difficulty: ρ=0.8 for difficult tasks vs. ρ=0.3 for easy tasks.
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## Key Finding
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**Adoption rates by difficulty (disclosed condition):**
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- Difficult tasks: ρ=0.8 correlation between AI exposure and adoption
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- Easy tasks: ρ=0.3 correlation between AI exposure and adoption
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- AI source was explicitly labeled in both conditions
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**Interpretation:**
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- Disclosure did not suppress AI adoption where participants most needed help (difficult tasks)
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- Participants appeared to use task difficulty as a heuristic for when to rely on AI
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- This suggests rational/strategic AI use rather than blind adoption or blanket rejection
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## Implications for Disclosure Policies
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This finding complicates simple "just disclose AI" policies:
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- Disclosure alone does not prevent AI reliance
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- Users may rationally choose to rely on AI when tasks are difficult
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- The question shifts from "does disclosure reduce AI use" to "when should AI use be encouraged/discouraged"
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## Scope Qualifiers
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- Single task type (Alternate Uses Task)
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- Experimental setting with explicit labeling
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- Self-reported adoption measures
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- Does not address long-term effects or skill atrophy
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- Does not compare disclosed vs. non-disclosed conditions across difficulty levels
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## Tension with Skill Development
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This finding creates tension with [[deep technical expertise is a greater force multiplier than AI assistance]] — if users adopt AI most on difficult tasks (where they most need to develop expertise), this could create a deskilling dynamic where AI prevents learning at precisely the difficulty level where learning is most valuable.
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The "rational" adoption pattern (use AI when tasks are hard) may be individually rational but collectively problematic if it prevents skill development.
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## Relevant Notes
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- Potential connection to AI deskilling literature (if claims exist in KB)
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- Flagged for implications on AI disclosure policy design
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---
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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]]
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---
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type: claim
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title: AI-generated ideas increase collective diversity in experimental creativity tasks
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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 a pre-registered experiment with 800+ participants across 40+ countries, exposure to AI-generated ideas increased collective diversity on the Alternate Uses Task, even as individual creativity metrics remained unchanged or decreased.
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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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- "[[partial connectivity produces better collective intelligence than full connectivity]]"
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- "[[collective intelligence requires diversity as a structural precondition]]"
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challenged_by:
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- "[[homogenization effect of large language models on creative diversity]]"
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---
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# AI-generated ideas increase collective diversity in experimental creativity tasks
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In a pre-registered experiment (N=810, 40+ countries), Doshi & Hauser (2025) found that exposure to AI-generated ideas increased collective diversity on the Alternate Uses Task, even though individual creativity metrics (fluency, flexibility, originality) remained unchanged or decreased.
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## Key Findings
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**Collective diversity increased with AI exposure:**
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- High AI exposure (10 AI ideas) produced significantly higher collective diversity than low exposure (2 AI ideas) or control conditions
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- Effect held across multiple diversity metrics (semantic distance, category coverage)
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- Individual-level creativity did not increase; the effect was purely collective
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**Mechanism: AI as external diversity source:**
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- AI ideas introduced variation orthogonal to human ideation patterns
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- Participants incorporated AI suggestions in idiosyncratic ways
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- The "multiple worlds" experimental design (each participant saw different AI ideas) prevented convergence
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**Scope qualifiers:**
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- Single task type (Alternate Uses Task)
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- Experimental setting with controlled AI exposure
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- Short-term effects only
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- Does not address naturalistic usage patterns
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## Challenges to Homogenization Narrative
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This finding appears to contradict studies showing AI homogenizes creative output (e.g., ScienceDirect 2025 study on LLM creative diversity). The key difference:
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- **Homogenization studies:** Naturalistic settings where users converge on similar AI outputs
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- **This study:** Controlled exposure where each participant receives different AI ideas
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Both findings can be true: AI can homogenize when users access the same outputs, but diversify when used as a source of varied external input.
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## Implications for Collective Intelligence
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This connects to [[partial connectivity produces better collective intelligence than full connectivity]] — AI may function as a controlled diversity injection mechanism, similar to how partial connectivity prevents premature convergence while maintaining enough information flow.
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The finding supports [[collective intelligence requires diversity as a structural precondition]] by demonstrating that external diversity sources (AI) can substitute for or complement human diversity in collective tasks.
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## Relevant Notes
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- [[deep technical expertise is a greater force multiplier than AI assistance]] — this finding cuts against simple skill-amplification stories; AI's value may be in diversity injection rather than individual capability enhancement
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- Flagged for Clay: implications for creative industries and entertainment production
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