The Power Of Suggestion

How recommendation algorithms run the world.

- Zeynep Tufekci

This March, a book that advances an outlandish conspiracy theory—a theory whose name I will not mention—soared in Amazon’s sales rankings. The book’s rise was helped greatly when the e-commerce giant put the book on its carousel of recommended titles, which is shown to shoppers who aren’t searching for that particular book. That fueled more curiosity and sales. Which led to more recommendations. Q The particular conspiracy theory outlined in this book holds that President Donald Trump pretended to collude with Russia precisely to ensure that he would be investigated, which would give him a chance to secretly collaborate with special prosecutor Robert Mueller to investigate and finally arrest former presidential candidate Hillary Clinton, who belongs to a global satanic cult of pedophiles with Barack Obama and George Soros. Yes, it’s that unhinged. So why is Amazon recommending this book to unsuspecting shoppers? It’s not because this theory has enormous persuasive power or a bestseller-size audience. Blame it on the tyranny of recommendation algorithms.

What should you watch? What should you read? What’s news? What’s trending? Wherever you go online, companies have come up with very particular, imperfect ways of answering these questions. Everywhere you look, recommendation engines offer striking examples of how values and judgments become embedded in algorithms and how algorithms can be gamed by strategic actors.

Consider a common, seemingly straightforward method of making suggestions: a recommendation based on what people “like you” have read, watched, or shopped for. What exactly is a person like me? Which dimension of me? Is it someone of the same age, gender, race, or location? Do they share my interests? My eye color? My height? Or is their resemblance to me determined by a whole mess of “big data” (aka surveillance) crunched by a machine-learning algorithm?