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AI Models Show Bias Based on Author's IdentityCEFR B1

25 Nov 2025

Adapted from U. Zurich, Futurity CC BY 4.0

Photo by Siora Photography, Unsplash

AI-assisted adaptation of the original article, simplified for language learners.

The recent study by University of Zurich researchers indicates that large language models (LLMs) can exhibit significant bias based on the perceived identity of authors. The study involved four major LLMs, which were tasked with creating and evaluating statements on controversial issues. When the authorship was anonymous, the LLMs showed over 90% agreement across evaluations. However, revealing an author's nationality uncovered deep biases, particularly against Chinese authors.

The research highlighted that LLMs had a tendency to trust human writers more than AI writers, suggesting an underlying distrust of machine-generated content. This raises important questions about reliance on AI for evaluating information, especially in sensitive areas like hiring or journalism. If these biases persist, they can lead to serious consequences in decision-making processes.

To mitigate such problems, it is essential to implement transparency and governance in how AI evaluates information before deploying it in critical social contexts. Ultimately, while AI can assist with reasoning, it should not be used as the final judge in important situations.

Difficult words

  • biasa tendency to favor one thing over another.
    biases
  • evaluateto judge or assess something.
    evaluating, evaluations
  • identityhow someone or something is described or recognized.
  • revealto make something known or visible.
    revealing
  • transparencyopenness and clarity about actions and decisions.
  • trustto believe in someone's reliability or truth.
  • consequencesresults or effects of an action.

Tip: hover, focus or tap highlighted words in the article to see quick definitions while you read or listen.

Discussion questions

  • How can biases in AI be addressed effectively?
  • What are the potential risks of using AI in decision-making?
  • In what ways can transparency improve AI trustworthiness?
  • How might AI's bias impact various social contexts?

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