How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas: Evidence From a Large, Dynamic Experiment

2024-01-24

arXiv.org

AI Summary

This study investigates how AI-generated creativity influences the collective evolution of human creative output. Through an online dynamic experiment spanning 40+ countries and involving 800+ participants, we introduce the novel “cultural cycle” paradigm: participants iteratively build upon prior creations—including AI-generated ones—to form multigenerational creative transmission chains. Semantic distance and conceptual novelty quantify diversity, while multilevel regression modeling reveals that high AI exposure significantly accelerates both the rate and total magnitude of group-level creative diversity, without improving individual creative quality. Disclosure of AI authorship exerts no main effect; however, participants with higher self-rated creativity exhibit greater resilience to AI-labeling bias, and AI-generated ideas are adopted more frequently in challenging tasks. The core contribution lies in demonstrating that AI integration into cultural evolutionary processes exerts a cumulative, group-level diversifying effect—enhancing collective creativity diversity rather than individual creative capacity.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Philosophy and Ethics of AI: Artificial General Intelligence

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Exposure to large language model output is rapidly increasing. How will seeing AI-generated ideas affect human ideas? We conducted an experiment (800+ participants, 40+ countries) where participants viewed creative ideas that were from ChatGPT or prior experimental participants and then brainstormed their own idea. We varied the number of AI-generated examples (none, low, or high exposure) and if the examples were labeled as 'AI' (disclosure). Our dynamic experiment design -- ideas from prior participants in an experimental condition are used as stimuli for future participants in the same experimental condition -- speaks to the interdependent process of cultural creation: creative ideas are built upon prior ideas. Hence, we capture the compounding effects of having LLMs 'in the culture loop'. We find that high AI exposure (but not low AI exposure) did not affect the creativity of individual ideas but did increase the average amount and rate of change of collective idea diversity. AI made ideas different, not better. There were no main effects of disclosure. We also found that self-reported creative people were less influenced by knowing an idea was from AI and that participants may knowingly adopt AI ideas when the task is difficult. Our findings suggest that introducing AI ideas may increase collective diversity but not individual creativity.

Problem

• How AI-generated ideas influence human creativity and diversity
• Effects of AI exposure levels on collective idea evolution
• Impact of AI idea disclosure on human creative adoption

Innovation

• Dynamic experiment design with interdependent cultural creation
• Varied AI exposure levels and disclosure conditions
• Measured collective diversity and individual creativity effects