mlsanityData to 14 Oct 2026

The Democratic Paradox in Large Language Models' Underestimation of Press Freedom

arXiv 2506.18045 ↗First posted 22 Jun 2025cs.CY cs.AI cs.CLcited by 0

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Abstract (arXiv)

As Large Language Models (LLMs) increasingly mediate global information access for millions of users worldwide, their alignment and biases have the potential to shape public understanding and trust in fundamental democratic institutions, such as press freedom. In this study, we uncover three systematic distortions in the way six popular LLMs evaluate press freedom in 180 countries compared to expert assessments of the World Press Freedom Index (WPFI). The six LLMs exhibit a negative misalignment, consistently underestimating press freedom, with individual models rating between 71% to 93% of countries as less free. We also identify a paradoxical pattern we term differential misalignment: LLMs disproportionately underestimate press freedom in countries where it is strongest. Additionally, five of the six LLMs exhibit positive home bias, rating their home countries' press freedoms more favorably than would be expected given their negative misalignment with the human benchmark. In some cases, LLMs rate their home countries between 7% to 260% more positively than expected. If LLMs are set to become the next search engines and some of the most important cultural tools of our time, they must ensure accurate representations of the state of our human and civic rights globally.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 36 references that are in the map. First posted on arXiv 22 Jun 2025.

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References · 9

  1. 2019The Woman Worked as a Babysitter: On Biases in Language Generationcited by 541
  2. 2023Towards Measuring the Representation of Subjective Global Opinions in Language Modelscited by 437
  3. 2023From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Modelscited by 260
  4. 2019Identifying and Reducing Gender Bias in Word-Level Language Modelscited by 214
  5. 2023Cultural Bias and Cultural Alignment of Large Language Modelscited by 149
  6. 2023Generative Language Models Exhibit Social Identity Biasescited by 111
  7. 2023Bias of AI-Generated Content: An Examination of News Produced by Large Language Modelscited by 38
  8. 2025Be Friendly, Not Friends: How LLM Sycophancy Shapes User Trustcited by 34
  9. 2025Echoes of Power: Investigating Geopolitical Bias in US and China Large Language Modelscited by 5

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