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AI Models Show Bias Based on Author's IdentityCEFR A2

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.

Researchers found that large language models (LLMs) evaluate texts differently based on the author’s identity. In a study, they used four popular LLMs. They asked these models to create statements on various topics and then evaluate them.

When the author’s nationality was revealed, biases appeared. For example, the models showed a strong anti-Chinese bias. They trusted human writers more than other AI systems. This means LLMs can react strongly to an author's background.

The results raise concerns about using AI for tasks like hiring or content moderation, as biases may lead to unfair judgments.

Difficult words

  • researcherA person who studies or investigates something.
    Researchers
  • evaluateTo judge or calculate the value or quality.
  • biasAn unfair preference or dislike.
    biases
  • nationalityThe status of belonging to a specific nation.
  • concernsWorries or issues that need attention.
  • judgmentsDecisions about someone or something.
  • moderationThe process of managing or controlling content.

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

Discussion questions

  • Why is it important to consider an author's background?
  • How can biases in AI affect hiring decisions?
  • In what other areas might AI evaluation cause problems?

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