Movatterモバイル変換


[0]ホーム

URL:


Skip to main content
Cornell University

Monday, May 5: arXiv will be READ ONLY at 9:00AM EST for approximately 30 minutes. We apologize for any inconvenience.

We gratefully acknowledge support from the Simons Foundation,member institutions, and all contributors.Donate
arxiv logo>cs> arXiv:2401.13481
arXiv logo
Cornell University Logo

Computer Science > Computers and Society

arXiv:2401.13481 (cs)
[Submitted on 24 Jan 2024 (v1), last revised 4 Jul 2024 (this version, v2)]

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

View PDFHTML (experimental)
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.
Subjects:Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
Cite as:arXiv:2401.13481 [cs.CY]
 (orarXiv:2401.13481v2 [cs.CY] for this version)
 https://doi.org/10.48550/arXiv.2401.13481
arXiv-issued DOI via DataCite

Submission history

From: Joshua Ashkinaze [view email]
[v1] Wed, 24 Jan 2024 14:29:39 UTC (12,538 KB)
[v2] Thu, 4 Jul 2024 17:14:24 UTC (9,085 KB)
Full-text links:

Access Paper:

Current browse context:
cs.CY
Change to browse by:
export BibTeX citation

Bookmark

BibSonomy logoReddit logo

Bibliographic and Citation Tools

Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
scite Smart Citations(What are Smart Citations?)

Code, Data and Media Associated with this Article

CatalyzeX Code Finder for Papers(What is CatalyzeX?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)

Demos

Hugging Face Spaces(What is Spaces?)

Recommenders and Search Tools

Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.

Which authors of this paper are endorsers? |Disable MathJax (What is MathJax?)

[8]ページ先頭

©2009-2025 Movatter.jp