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
- researcher — A person who studies or investigates something.Researchers
- evaluate — To judge or calculate the value or quality.
- bias — An unfair preference or dislike.biases
- nationality — The status of belonging to a specific nation.
- concerns — Worries or issues that need attention.
- judgments — Decisions about someone or something.
- moderation — The 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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