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Out of One, Many: Using Language Models to Simulate Human Samples

Published online by Cambridge University Press:  21 February 2023

Lisa P. Argyle Open the ORCID record for Lisa P. Argyle [Opens in a new window] ,
Ethan C. Busby ,
Nancy Fulda ,
Joshua R. Gubler Open the ORCID record for Joshua R. Gubler [Opens in a new window] ,
Christopher Rytting  and
David Wingate
Lisa P. Argyle*
Affiliation:
Department of Political Science, Brigham Young University, Provo, UT, USA. e-mail: lpargyle@byu.edu, ethan.busby@byu.edu, jgub@byu.edu
Ethan C. Busby
Affiliation:
Department of Political Science, Brigham Young University, Provo, UT, USA. e-mail: lpargyle@byu.edu, ethan.busby@byu.edu, jgub@byu.edu
Nancy Fulda
Affiliation:
Department of Computer Science, Brigham Young University, Provo, UT, USA. e-mail: nfulda@cs.byu.edu, christophermichaelrytting@gmail.com, wingated@cs.byu.edu
Joshua R. Gubler
Affiliation:
Department of Political Science, Brigham Young University, Provo, UT, USA. e-mail: lpargyle@byu.edu, ethan.busby@byu.edu, jgub@byu.edu
Christopher Rytting
Affiliation:
Department of Computer Science, Brigham Young University, Provo, UT, USA. e-mail: nfulda@cs.byu.edu, christophermichaelrytting@gmail.com, wingated@cs.byu.edu
David Wingate
Affiliation:
Department of Computer Science, Brigham Young University, Provo, UT, USA. e-mail: nfulda@cs.byu.edu, christophermichaelrytting@gmail.com, wingated@cs.byu.edu
*
Corresponding author Lisa P. Argyle
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Abstract

We propose and explore the possibility that language models can be studied as effective proxies for specific human subpopulations in social science research. Practical and research applications of artificial intelligence tools have sometimes been limited by problematic biases (such as racism or sexism), which are often treated as uniform properties of the models. We show that the “algorithmic bias” within one such tool—the GPT-3 language model—is instead both fine-grained and demographically correlated, meaning that proper conditioning will cause it to accurately emulate response distributions from a wide variety of human subgroups. We term this property algorithmic fidelity and explore its extent in GPT-3. We create “silicon samples” by conditioning the model on thousands of sociodemographic backstories from real human participants in multiple large surveys conducted in the United States. We then compare the silicon and human samples to demonstrate that the information contained in GPT-3 goes far beyond surface similarity. It is nuanced, multifaceted, and reflects the complex interplay between ideas, attitudes, and sociocultural context that characterize human attitudes. We suggest that language models with sufficient algorithmic fidelity thus constitute a novel and powerful tool to advance understanding of humans and society across a variety of disciplines.

Keywords

artificial intelligencemachine learningcomputational social sciencepublic opinion
Type
Article
Information
Political Analysis , Volume 31 , Issue 3 , July 2023 , pp. 337 - 351
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of the Society for Political Methodology

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Footnotes

Edited by Jeff Gill

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