ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2024
Large language models (LLMs) as agents for augmented democracy.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Generative AI voting: fair collective choice is resilient to LLM biases and inconsistencies.EPJ data science · 2026Article
- Generative AI for climate governance and acceptability-constrained policy design.npj climate action · 2026Review
- AMULED: Addressing Moral Uncertainty using Large language models for Ethical Decision-making.Frontiers in artificial intelligence · 2026Article
- Adaptive political surveys and GPT-4: Tackling the cold start problem with simulated user interactions.PloS one · 2025Article
- Co-creating the future: participatory cities and digital governance.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2024Article
- Large language models (LLMs) as agents for augmented democracy.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.