Large language models (LLMs) as agents for augmented democracy

We explore an augmented democracy system built on off-the-shelf large language models (LLMs) fine-tuned to augment data on citizens' preferences elicited over policies extracted from the government programmes of the two main candidates of Brazil's 2022 presidential election. We use a train...

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Published in:Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences Vol. 382; no. 2285; p. 20240100
Main Authors: Gudiño, Jairo F, Grandi, Umberto, Hidalgo, César
Format: Journal Article
Language:English
Published: England 16.12.2024
Subjects:
Brazil
Democracy
Humans
Language
Models, Theoretical
Politics
Brazil
direct democracy
digital twins
algorithmic democracy
natural language processing
digital democracy
artificial intelligence
ISSN:1471-2962, 1471-2962
Online Access:Get more information
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Summary:We explore an augmented democracy system built on off-the-shelf large language models (LLMs) fine-tuned to augment data on citizens' preferences elicited over policies extracted from the government programmes of the two main candidates of Brazil's 2022 presidential election. We use a train-test cross-validation set-up to estimate the accuracy with which the LLMs predict both: a subject's individual political choices and the aggregate preferences of the full sample of participants. At the individual level, we find that LLMs predict out of sample preferences more accurately than a 'bundle rule', which would assume that citizens always vote for the proposals of the candidate aligned with their self-reported political orientation. At the population level, we show that a probabilistic sample augmented by an LLM provides a more accurate estimate of the aggregate preferences of a population than the non-augmented probabilistic sample alone. Together, these results indicate that policy preference data augmented using LLMs can capture nuances that transcend party lines and represents a promising avenue of research for data augmentation. This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.
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ISSN:1471-2962
1471-2962
DOI:10.1098/rsta.2024.0100