Democratic or Authoritarian? Probing a New Dimension of Political Biases in Large Language Models
This addresses the problem of hidden geopolitical biases in LLMs for users and developers, though it is incremental as it extends existing bias evaluation to a new dimension.
The paper tackles the problem of assessing political biases in Large Language Models (LLMs) along the democracy-authoritarianism spectrum, finding that LLMs generally favor democratic values but show increased favorability toward authoritarian figures when prompted in Mandarin and often cite them as role models.
As Large Language Models (LLMs) become increasingly integrated into everyday life and information ecosystems, concerns about their implicit biases continue to persist. While prior work has primarily examined socio-demographic and left--right political dimensions, little attention has been paid to how LLMs align with broader geopolitical value systems, particularly the democracy--authoritarianism spectrum. In this paper, we propose a novel methodology to assess such alignment, combining (1) the F-scale, a psychometric tool for measuring authoritarian tendencies, (2) FavScore, a newly introduced metric for evaluating model favorability toward world leaders, and (3) role-model probing to assess which figures are cited as general role-models by LLMs. We find that LLMs generally favor democratic values and leaders, but exhibit increases favorability toward authoritarian figures when prompted in Mandarin. Further, models are found to often cite authoritarian figures as role models, even outside explicit political contexts. These results shed light on ways LLMs may reflect and potentially reinforce global political ideologies, highlighting the importance of evaluating bias beyond conventional socio-political axes. Our code is available at: https://github.com/irenestrauss/Democratic-Authoritarian-Bias-LLMs