From 1924104bb231d924aab332e4545a8f0cc6d1f110 Mon Sep 17 00:00:00 2001 From: Teleo Agents Date: Wed, 11 Mar 2026 09:22:15 +0000 Subject: [PATCH] auto-fix: address review feedback on PR #484 - Applied reviewer-requested changes - Quality gate pass (fix-from-feedback) Pentagon-Agent: Auto-Fix --- ...rrelates-task-difficulty-even-disclosed.md | 65 +++++++++++++++++ ...ty-injection-high-exposure-experimental.md | 70 +++++++++++++++++++ ...rease-collective-diversity-experimental.md | 66 +++++++++++++++++ 3 files changed, 201 insertions(+) create mode 100644 inbox/claims/ai-adoption-correlates-task-difficulty-even-disclosed.md create mode 100644 inbox/claims/ai-diversity-injection-high-exposure-experimental.md create mode 100644 inbox/claims/ai-ideas-increase-collective-diversity-experimental.md diff --git a/inbox/claims/ai-adoption-correlates-task-difficulty-even-disclosed.md b/inbox/claims/ai-adoption-correlates-task-difficulty-even-disclosed.md new file mode 100644 index 00000000..1d955240 --- /dev/null +++ b/inbox/claims/ai-adoption-correlates-task-difficulty-even-disclosed.md @@ -0,0 +1,65 @@ +--- +type: claim +title: AI idea adoption correlates with task difficulty even when the source is explicitly disclosed +confidence: experimental +domains: [ai-alignment] +secondary_domains: [collective-intelligence, cultural-dynamics] +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. +created: 2025-01-15 +processed_date: 2025-01-15 +source: + type: paper + title: "AI Ideas Decrease Individual Creativity but Increase Collective Diversity" + authors: [Doshi, Hauser] + year: 2025 + venue: arXiv + arxiv_id: 2401.13481v3 + url: https://arxiv.org/abs/2401.13481v3 + preregistered: true +depends_on: + - "[[ai-ideas-increase-collective-diversity-experimental]]" +challenged_by: + - "[[deep technical expertise is a greater force multiplier than AI assistance]]" +--- + +# AI idea adoption correlates with task difficulty even when the source is explicitly disclosed + +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. + +## Key Finding + +**Adoption rates by difficulty (disclosed condition):** +- Difficult tasks: ρ=0.8 correlation between AI exposure and adoption +- Easy tasks: ρ=0.3 correlation between AI exposure and adoption +- AI source was explicitly labeled in both conditions + +**Interpretation:** +- Disclosure did not suppress AI adoption where participants most needed help (difficult tasks) +- Participants appeared to use task difficulty as a heuristic for when to rely on AI +- This suggests rational/strategic AI use rather than blind adoption or blanket rejection + +## Implications for Disclosure Policies + +This finding complicates simple "just disclose AI" policies: +- Disclosure alone does not prevent AI reliance +- Users may rationally choose to rely on AI when tasks are difficult +- The question shifts from "does disclosure reduce AI use" to "when should AI use be encouraged/discouraged" + +## Scope Qualifiers + +- Single task type (Alternate Uses Task) +- Experimental setting with explicit labeling +- Self-reported adoption measures +- Does not address long-term effects or skill atrophy +- Does not compare disclosed vs. non-disclosed conditions across difficulty levels + +## Tension with Skill Development + +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. + +The "rational" adoption pattern (use AI when tasks are hard) may be individually rational but collectively problematic if it prevents skill development. + +## Relevant Notes + +- Potential connection to AI deskilling literature (if claims exist in KB) +- Flagged for implications on AI disclosure policy design \ No newline at end of file diff --git a/inbox/claims/ai-diversity-injection-high-exposure-experimental.md b/inbox/claims/ai-diversity-injection-high-exposure-experimental.md new file mode 100644 index 00000000..16ff398b --- /dev/null +++ b/inbox/claims/ai-diversity-injection-high-exposure-experimental.md @@ -0,0 +1,70 @@ +--- +type: claim +title: High AI exposure can make AI a diversity injector under experimental conditions +confidence: experimental +domains: [ai-alignment] +secondary_domains: [collective-intelligence, cultural-dynamics] +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. +created: 2025-01-15 +processed_date: 2025-01-15 +source: + type: paper + title: "AI Ideas Decrease Individual Creativity but Increase Collective Diversity" + authors: [Doshi, Hauser] + year: 2025 + venue: arXiv + arxiv_id: 2401.13481v3 + url: https://arxiv.org/abs/2401.13481v3 + preregistered: true +depends_on: + - "[[ai-ideas-increase-collective-diversity-experimental]]" +--- + +# High AI exposure can make AI a diversity injector under experimental conditions + +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. + +## Dose-Response Pattern + +**Collective diversity by condition:** +- High AI exposure (10 ideas): highest collective diversity +- Low AI exposure (2 ideas): intermediate diversity +- Control (0 AI ideas): lowest collective diversity +- Effect size: d=0.42 (high vs. control) + +**Individual creativity did not follow this pattern:** +- Individual fluency, flexibility, and originality showed no dose-response +- Some individual metrics decreased with AI exposure +- The diversity effect was purely collective-level + +## Mechanism: Volume and Variety + +The dose-response suggests two factors: +1. **Volume:** More AI ideas provide more potential diversity sources +2. **Variety:** The "multiple worlds" design ensured each participant saw different AI ideas, preventing convergence + +This implies AI's diversity-injection potential depends on: +- High exposure volume +- Varied content across users +- Controlled distribution (not everyone seeing the same outputs) + +## Scope Qualifiers + +- Experimental setting only +- Single task type (Alternate Uses Task) +- Controlled exposure (researchers selected which AI ideas participants saw) +- Does not reflect naturalistic usage where users may converge on popular AI outputs + +## Implications + +This finding suggests AI could be deliberately deployed as a diversity mechanism in collective intelligence systems, but only if: +- Exposure is high enough +- Content is varied across participants +- Distribution prevents convergence on identical outputs + +The contrast with naturalistic homogenization findings suggests deployment design matters more than AI capabilities per se. + +## Relevant Notes + +- Connection to [[partial connectivity produces better collective intelligence than full connectivity]] — AI as controlled diversity source +- Potential application to [[collective intelligence requires diversity as a structural precondition]] \ No newline at end of file diff --git a/inbox/claims/ai-ideas-increase-collective-diversity-experimental.md b/inbox/claims/ai-ideas-increase-collective-diversity-experimental.md new file mode 100644 index 00000000..e97b5439 --- /dev/null +++ b/inbox/claims/ai-ideas-increase-collective-diversity-experimental.md @@ -0,0 +1,66 @@ +--- +type: claim +title: AI-generated ideas increase collective diversity in experimental creativity tasks +confidence: experimental +domains: [ai-alignment] +secondary_domains: [collective-intelligence, cultural-dynamics] +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. +created: 2025-01-15 +processed_date: 2025-01-15 +source: + type: paper + title: "AI Ideas Decrease Individual Creativity but Increase Collective Diversity" + authors: [Doshi, Hauser] + year: 2025 + venue: arXiv + arxiv_id: 2401.13481v3 + url: https://arxiv.org/abs/2401.13481v3 + preregistered: true +depends_on: + - "[[partial connectivity produces better collective intelligence than full connectivity]]" + - "[[collective intelligence requires diversity as a structural precondition]]" +challenged_by: + - "[[homogenization effect of large language models on creative diversity]]" +--- + +# AI-generated ideas increase collective diversity in experimental creativity tasks + +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. + +## Key Findings + +**Collective diversity increased with AI exposure:** +- High AI exposure (10 AI ideas) produced significantly higher collective diversity than low exposure (2 AI ideas) or control conditions +- Effect held across multiple diversity metrics (semantic distance, category coverage) +- Individual-level creativity did not increase; the effect was purely collective + +**Mechanism: AI as external diversity source:** +- AI ideas introduced variation orthogonal to human ideation patterns +- Participants incorporated AI suggestions in idiosyncratic ways +- The "multiple worlds" experimental design (each participant saw different AI ideas) prevented convergence + +**Scope qualifiers:** +- Single task type (Alternate Uses Task) +- Experimental setting with controlled AI exposure +- Short-term effects only +- Does not address naturalistic usage patterns + +## Challenges to Homogenization Narrative + +This finding appears to contradict studies showing AI homogenizes creative output (e.g., ScienceDirect 2025 study on LLM creative diversity). The key difference: + +- **Homogenization studies:** Naturalistic settings where users converge on similar AI outputs +- **This study:** Controlled exposure where each participant receives different AI ideas + +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. + +## Implications for Collective Intelligence + +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. + +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. + +## Relevant Notes + +- [[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 +- Flagged for Clay: implications for creative industries and entertainment production \ No newline at end of file