One of the most common applications of machine learning today is in recommendation algorithms. Netflix and YouTube use them to push you new shows and videos; Google and Facebook use them to rank the content in your search results and news feed. While these algorithms offer a great deal of convenience, they have some undesirable side effects. You’ve probably heard of them before: filter bubbles and echo chambers.
Concern about these effects is not new. In 2011, Eli Pariser, now the CEO of Upworthy, warned about filter bubbles on the TED stage. Even before that, in his book Republic.com, Harvard law professor Cass Sunstein accurately predicted a “group polarization” effect, driven by the rise of the Internet, that would ultimately challenge a healthy democracy. Facebook wouldn’t exist for another three years.
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Both ideas were quickly popularized in the aftermath of the 2016 US election, which led to an upswell of relevant research. Now Google’s own AI subsidiary, DeepMind, is adding to the body of scholarship. (Better late than never, right?)
In a new paper, researchers analyzed how different recommendation algorithms can speed up or slow down both phenomena, which the researchers define separately. Echo chambers, they say, reinforce users’ interests through repeated exposure to similar content. Filter bubbles, by comparison, narrow the scope of content users are exposed to. Despite making that distinction, the researchers acknowledge that they are the same type of thing—which they refer to in academic-speak as “degenerate feedback loops.” A higher level of degeneracy, in this case, refers to a stronger filter bubble or echo chamber effect.
They ran simulations of five different recommendation algorithms, which placed different degrees of priority on accurately predicting exactly what the user was interested in over randomly promoting new content. The algorithms that prioritized accuracy more highly, they found, led to much faster system degeneracy. In other words, the best way to combat filter bubbles or echo chambers is to design the algorithms to be more exploratory, showing you things that are less certain to capture your interest. Expanding the overall set of information from which the recommendations are drawn from can also help.
Joseph A. Konstan, a computer science professor at the University of Minnesota, who has previously conducted research on filter bubbles, says the results from DeepMind’s analysis are not surprising. Researchers have long understood the tension between accurate prediction and effective exploration in recommendation systems, he says.
Despite past studies showing that users will tolerate lower levels of accuracy to gain the benefit of diverse recommendations, developers still have a disincentive to design their algorithms that way. “It is always easier to ‘be right’ by recommending safe choices,” Konstan says.
Konstan also critiques the DeepMind study for approaching filter bubbles and echo chambers as machine-learning simulations rather than interactive systems involving humans—a limitation the researchers noted as well. “I am always concerned about work that is limited to simulation studies (or offline data analyses),” he says. “People are complex. On the one hand we know they value diversity, but on the other hand we also know that if we stretch the recommendations too far—to the point where users feel we are not trustworthy—we may lose the users entirely.”
