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.

翻译：随着大型语言模型（LLMs）日益成为全球数百万用户获取信息的中介，其对齐性与偏见可能影响公众对新闻自由等基本民主制度的理解与信任。本研究揭示了六种主流LLMs在评估180个国家新闻自由时，相较于世界新闻自由指数（WPFI）的专家评估，存在的三种系统性偏差。这六种LLMs均呈现负向错位，持续低估新闻自由水平，各模型将71%至93%的国家评估为自由度较低。我们还发现一种矛盾模式，称之为差异错位：LLMs对新闻自由度最高国家的低估程度尤为显著。此外，其中五种LLMs表现出正向本土偏差，即使在与人类基准存在负向错位的情况下，仍对本国新闻自由给予超出预期的积极评价。在某些案例中，LLMs对本国的评价比预期高出7%至260%。若LLMs将成为下一代搜索引擎乃至当代最重要的文化工具之一，其必须确保能准确呈现全球人权与公民权利的真实状况。