Personalized recommendation algorithms can trap your learning in a filter bubble

What is it about?

Recommendation algorithms, which are broadly used to personalize online content, are discussed as a source of "filter bubbles", guiding you only to information that fits your interests or opinions. Our study investigates how algorithmically recommended information changes the way people learn, understand, and think about the world.

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Why is it important?

Our study shows that even without preexisting opinions or attitudes, interactions with recommendation algorithms can gradually bias how people learn and narrow what they explore. People may confidently apply distorted or incomplete knowledge to new situations where it no longer fits.