The modern internet is powered by recommendation algorithms. They’re everywhere from Facebook to YouTube, from search engines to shopping websites. These systems track your online consumption and use that data to suggest the next piece of content for you to absorb. Their goal is to keep users on a platform by presenting them with things they’ll spend more time engaging with. Trouble is, those link chains can lead to some weird places, occasionally taking users down dark internet rabbit holes or showing harmful content. Lawmakers and researchers have criticized recommendation systems before, but these methods are under renewed scrutiny now that Google and Twitter are going before the US Supreme Court to defend their algorithmic practices.
This week on Gadget Lab, we talk with Jonathan Stray, a senior scientist at the Berkeley Center for Human-Compatible AI who studies recommendation systems online. We discuss how recommendation algorithms work, how they’re studied, and how they can be both abused and restrained.
Show Notes
Read all about Section 230. Read Jonathan Stray and Gillian Hadfield’s story on WIRED about their engagement research. Read more about the two cases before the US Supreme Court.
Recommendations
Jonathan recommends the book The Way Out by Peter Coleman. Mike recommends the novel Denial by Jon Raymond. Lauren recommends Matt Reynolds’ WIRED story about how you’ve been thinking about food all wrong, and also getting a bag to make nut milk.
Jonathan Stray can be found on Twitter @jonathanstray. Lauren Goode is @LaurenGoode. Michael Calore is @snackfight. Bling the main hotline at @GadgetLab. The show is produced by Boone Ashworth (@booneashworth). Our theme music is by Solar Keys.
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