---
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]]