Filterworld: How Algorithms Flattened Culture

by Kyle Chayka

Marshall McLuhan wrote his famous dictum “the medium is the message” in his 1964 book Understanding Media: The Extensions of Man. He meant that the structure of a new medium—electric light, the telephone, television—is more important than the content that travels through it. The telephone’s ability to connect people exceeds any particular conversation. “The ‘message’ of any medium or technology is the change of scale or pace or pattern that it introduces into human affairs,” McLuhan wrote. In our case, the medium is the algorithmic feed; it has scaled and sped up humanity’s interconnection across the world to an unimaginable degree. Its message is that on some level, our collective consumption habits, translated into data, run together into sameness.

Boffone’s experience follows a fundamental rule of Filterworld: Under algorithmic feeds, the popular becomes more popular, and the obscure becomes even less visible.

There are two forces forming our tastes. As I described previously, the first is our independent pursuit of what we individually enjoy, while the second is our awareness of what it appears that most other people like, the dominant mainstream.

“Platforms have offered us up something that is meant for intimate, connected speech. Then they moderate it in a statistically broad, systematic way,” Gillespie told me. There’s little room for considering the specific nature of a given piece of content, since it lacks context and becomes atomized in the overall feed. That means we have also developed expectations about how the content we post travels online—namely, we most often expect it to travel as far as possible. “People have developed a notion not just that they get to speak, but there’s some expectation of where it’s supposed to go,” Gillespie told me. In other words, we have come to expect algorithmic promotion almost as a right. But perhaps this expectation needs to change, and we need to learn to exist in a digital world without so much automated acceleration. Reaching wide audiences of strangers isn’t a right; it’s a privilege that doesn’t need to be possible for every individual user or post. The word amplification describes algorithmic recommendations’ role in spreading content more widely than it would otherwise travel: like a megaphone, it turns regular speech into a shout. Amplification is at the core of Filterworld’s problems; one format gets amplified above all. Using regulation to target amplification could create more balance in the ecosystem.

If recommendations are to be regulated, certain decisions have to be made based on content. “Which preferences are unacceptable and must be overridden by law?” Keller asked. “Say platforms stop giving people what they want; you have to give them something else instead. What are the veggies, what information are we forcing into the diet, and how do we decide what that is? I don’t know what the answer is,” Keller said. She has suggested a system of “circuit breakers” that are “content neutral.” Rather than deciding which content is positive or negative, any kind of content that goes viral—accelerating quickly via recommendations—would be limited instead of promoted more, slowing down its spread and giving moderators more time to judge if it’s appropriate before it reaches massive audiences. Circuit breakers could also bring us back to a less globalized media ecosystem, when pieces of content stayed more firmly within their original contexts. While this might limit innocuous content like funny videos, it also means that Russell might not have been exposed to many of the memes that harmed her. She wouldn’t have gotten that email of Pinterest “Depression Pins” urging her deeper down the rabbit hole. Decisions about content ultimately have to be made by humans, both because machine-learning systems are not yet capable of making such subtle distinctions and because their consequences impact human lives. That human capacity is not scalable in the way that software is—one person ca...

In terms of how culture reaches us, algorithmic recommendations have supplanted the human news editor, the retail boutique buyer, the gallery curator, the radio DJ—people whose individual taste we relied on to highlight the unusual and the innovative. Instead, we have tech companies dictating the priorities of the recommendations, which are subjugated to generating profit through advertising. In Filterworld, the most popular culture is also the most desiccated. It is streamlined and averaged until, like a vitamin pill, it may contain the necessary ingredients but lacks any sense of brilliance or vitality.