DOI: 10.5061/dryad.qfttdz0pm
title: Generative AI enhances individual creativity but reduces the collective diversity of novel content

description: 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 practitioners interested in bolstering creativity.
description: 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, the stories composed by the writers are then
 evaluated by a separate group of N=600 participants (“evaluators”).
 Evaluators read six randomly selected stories without being informed about
 writers being randomly assigned to access GenAI in some conditions (or
 not). All stories are evaluated by multiple evaluators on novelty,
 usefulness, and several emotional characteristics. After disclosing to
 evaluators whether GenAI was used during the creative process, we ask
 evaluators to rate the extent to which ownership and hypothetical profits
 should be split between the writer and the AI. Finally, we elicit
 evaluators’ general views on the extent to which they believe that the use
 of AI in producing creative output is ethical, how story ownership and
 hypothetical profits should be shared between AI creators and human
 creators, and how AI should be credited in the involvement of the creative
 output. The data was collected on the online study
 platform Prolific. The data was then cleaned, processed and analyzed with
 Stata. For the Writer Study, of the 500 participants who began the study,
 169 exited the study prior to giving consent, 22 were dropped for not
 giving consent, and 13 dropped out prior to completing the study. Three
 participants in the Human only condition admitted to using GenAI during
 their story writing exercise and—as per our pre-registration—they were
 therefore dropped from the analysis, resulting in a total number of
 writers and stories of 293. For the Evaluator Study, each evaluator was
 shown 6 stories (2 stories from each topic). The evaluations associated
 with the writers who did not complete the writer study and those in the
 Human only condition who acknowledged using AI to complete the story were
 dropped. Thus, there are a total of 3,519 evaluations of 293 stories made
 by 600 evaluators. Four evaluations remained for five evaluators, five
 evaluations remained for 71, and all six remained for 524
 evaluators.
description: # 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 necessary to install these packages from within Stata. Do so by first
 entering the Python environment in Stata using the `python` command and
 then `pip install numpy` and so forth. 3. Ensure Stata is able to call
 Python (see `help python` Stata help documentation). 4. Download
 **`dat.py`** from
 [https://github.com/jayolson/divergent-association-task](https://github.com/jayolson/divergent-association-task). Place file in **`scripts`** folder. 5. Download **`words.txt`** from [https://github.com/jayolson/divergent-association-task](https://github.com/jayolson/divergent-association-task). Place file in **`scripts/words_glove`** folder. 6. Download and extract **`glove.840B.300d.zip`** from [https://nlp.stanford.edu/projects/glove/](https://nlp.stanford.edu/projects/glove/). Place file in **`scripts/words_glove`** folder. 7. Obtain an OpenAI API key (see [https://platform.openai.com/docs/api-reference/introduction](https://platform.openai.com/docs/api-reference/introduction)). 8. Install the following packages in Stata: * coefplot * colorpalette * dstat * estout * grc1leg2 * marginsplot * moremata (version 2.0.1 or newer) * violinplot ### Files setup 1. In **`scripts/00_control_center.do`**, change the following: * Change local macro `mywd` to the filepath of the **`GenAI_creativity_scripts`** folder. * Set local macro `compile_type` to "all" (`local compile_type = "all"`). 2. In **`scripts/ai_human_story_similarity.py`**, change the following: * Change the `openai.api_key` variable by replacing 'XXX' with your API key. 3. In compute\_dats.py, change the following: * Change the genai\_folder variable to the filepath of the **`GenAI_creativity_scripts`** folder. 4. Run the **`scripts/00_control_center.do`** file. *Important note*: Sometimes the call to the OpenAI API will fail. If that occurs, the do file will quit with an error. It will be necessary to rerun the script. ## 2\. Processed file analysis If you would like to use the already processed dta files (found in the **`already_processed_data`** folder) and perform the analysis, please follow these steps to ensure your machine is setup to run all the necessary scripts. ### Machine setup 1. Install the following packages in Stata: * coefplot * colorpalette * dstat * estout * grc1leg2 * marginsplot * moremata (version 2.0.1 or newer) * violinplot ### Files setup 1. In **`scripts/00_control_center.do`**, change the following: * Change local macro `mywd` to the filepath of the **`GenAI_creativity_scripts`** folder. * Set local macro `compile_type` to "analysis" (`local compile_type = "analysis"`). 2. Run the **`scripts/00_control_center.do`** file.