Democratic Value Trade-offs in Large Language Models: A Conjoint Audit Against Human Choices

Large language models (LLMs) summarize public consultations, mediate deliberation, and simulate citizens, so how they weigh democratic institutions against other outcomes depends on the political judgments they inform. People's stated support for democracy is close to universal, so political science measures their commitment by what they would give up democratic institutions for. Conjoint experiments do this by setting institutions such as free elections against outcomes such as a strong economy. Studies of LLMs values elicit stated positions or weigh one value against another, and none compares how LLMs make this trade-off with how people make it. We present the tasks of such an experiment, answered by 4,130 respondents in four countries, to 23 LLMs, each prompted with the background of the respondent who faced the task, and compare the trade-offs implied by the models' choices with the respondents'. Most models penalize unfair elections less than the respondents do and a low-income economy and placement in the smallest minority group more than a logistic regression fitted to the respondents' choices. In 19 of 23 models, a high-income economy outranks low corruption as compensation for a democratic loss, reversing the respondents' order, and all 16 models that predict individual choices best show the reversal. The reversal recurs across eight model families and in 17 of 23 models under a richer persona, free-text answers keep the smaller penalty on unfair elections, and fine-tuning three open models on the respondents' choices restores the corruption–economy order and reduces the error in their choice probabilities. Overall, most models, including the most accurate, accept unfair elections in return for a high-income economy more often than the respondents, so a model can agree with people's individual choices without sharing their democratic priorities.