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DeepMind is Asking How AI Helped Turn the Internet into an Echo Chamber


Visual representation of an echo chamber.

DeepMind researchers have analyzed how recommendation algorithms can accelerate or decelerate filter bubbles and echo chambers online.

Credit: David Pakman Show

Researchers at Google's DeepMind have analyzed how different recommendation algorithms can accelerate or decelerate filter bubbles and echo chambers online.

Filter bubbles narrow the scope of content users are exposed to, while echo chambers reinforce users' interests through repeated exposure to similar content.

Both phenomena are examples of "degenerate feedback loops." In this case, a higher level of degeneracy refers to a stronger filter bubble or echo chamber effect.

The researchers ran simulations of five different recommendation algorithms, each of which placed a different priority level on accurately predicting exactly what the user was interested in over randomly promoting new content.

The algorithms that prioritized accuracy more highly led to much faster system degeneracy—the best way to combat filter bubbles or echo chambers is to design the algorithms to be more exploratory, showing the user things that are less certain to capture interest.

From Technology Review
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Abstracts Copyright © 2019 SmithBucklin, Washington, DC, USA


 

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