teleo-codex/inbox/claims/ai-diversity-injection-high-exposure-experimental.md

type	title	confidence	domains	secondary_domains	description	created	processed_date	source	depends_on
claim	High AI exposure can make AI a diversity injector under experimental conditions	experimental	ai-alignment	collective-intelligence cultural-dynamics	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.	2025-01-15	2025-01-15	type title authors year venue arxiv_id url preregistered paper AI Ideas Decrease Individual Creativity but Increase Collective Diversity Doshi Hauser 2025 arXiv 2401.13481v3 https://arxiv.org/abs/2401.13481v3 true	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