Generative AI enhances individual creativity but reduces the collective diversity of novel content

Creativity is core to being human. Generative AI—made readily available by powerful large language models (LLMs)—holds promise for humans to be more creative by offering new ideas, or less creative by anchoring on generative AI ideas. We study the causal impact of generative AI ideas on the production of short stories in an online experiment where some writers obtained story ideas from an LLM. We find that access to generative AI ideas causes stories to be evaluated as more creative, better written, and more enjoyable, especially among less creative writers. However, generative AI-enabled stories are more similar to each other than stories by humans alone. These results point to an increase in individual creativity at the risk of losing collective novelty. This dynamic resembles a social dilemma: with generative AI, writers are individually better off, but collectively a narrower scope of novel content is produced. Our results have implications for researchers, policy-makers, and practi..., This dataset is based on a pre-registered, two-phase experimental online study. In the first phase of our study, we recruited a group of N=293 participants (“writers”) who are asked to write a short, eight sentence story. Participants are randomly assigned to one of three conditions: Human only, Human with 1 GenAI idea, and Human with 5 GenAI ideas. In our Human only baseline condition, writers are assigned the task with no mention of or access to GenAI. In the two GenAI conditions, we provide writers with the option to call upon a GenAI technology (OpenAI’s GPT-4 model) to provide a three-sentence starting idea to inspire their own story writing. In one of the two GenAI conditions (Human with 5 GenAI ideas), writers can choose to receive up to five GenAI ideas, each providing a possibly different inspiration for their story. After completing their story, writers are asked to self-evaluate their story on novelty, usefulness, and several emotional characteristics.
In the second phase, th..., , # Dataset and Code for \"Generative artificial intelligence enhances creativity but reduces the diversity of novel content\"

### by Anil R. Doshi and Oliver P. Hauser

## Introduction

We recommend downloading the file \"GenAI\_creativity\_data\_and\_scripts.zip\" which contains all data (raw and processed) as well as the analysis code. Then please follow the steps below.

We provide two methods to perform the data analysis.

1. Compile all files. This method processes the raw csv files and performs the analysis. Requires some knowledge of Python and an API key to OpenAI.
2. Processed file analysis. This allows you to run the analysis on the already processed dta files.

## 1\. Compile all files method

If you would like to compile all files, please follow these steps to ensure your machine is set up to run all the necessary scripts.

### Machine setup

1. Ensure Python is installed (tested with Python 3)
2. Install the following packages in Python

* numpy
* scipy
* openai

It may be nece...