---
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