The Democratic Paradox in Large Language Models' Underestimation of Press Freedom
I. Loaiza, R. Vestrelli, A. Fronzetti Colladon, R. Rigobon

TL;DR
This paper reveals systematic biases in large language models' assessments of press freedom, showing they underestimate global press freedom, especially in countries with strong press freedom, and display home bias, which could distort public understanding.
Contribution
It uncovers three systematic distortions in LLM evaluations of press freedom, highlighting underestimation, differential misalignment, and home bias, which are novel findings in the context of AI and democratic institutions.
Findings
LLMs underestimate press freedom in 71-93% of countries.
LLMs disproportionately underestimate press freedom where it is strongest.
Most LLMs show positive home bias, rating their own country's press freedom more favorably.
Abstract
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…
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Taxonomy
MethodsSparse Evolutionary Training
