Democratic or Authoritarian? Probing a New Dimension of Political Biases in Large Language Models

David Guzman Piedrahita, Irene Strauss, Bernhard Schölkopf, Rada Mihalcea, Zhijing Jin

Track: previously published paper

Keywords: model bias/fairness evaluation, ethical considerations in NLP applications, transparency

TL;DR: We introduce a framework to assess LLM bias along the democracy–authoritarianism spectrum. We find a pro-democratic slant in English, but increased favorability towards authoritarianism in Mandarin.

Abstract:

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 increased favorability toward authoritarian figures when prompted in Mandarin.
Further, models are found to often cite authoritarian figures as role models, even outside explicitly 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.

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Submission Number: 16