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LinkedIn Ran Social Experiments on 20 Million Users Over Five Years

LinkedIn ran experiments on more than 20 million users over five years that, while intended to improve how the platform worked for members, could have affected some people’s livelihoods, according to a new study.

In experiments conducted around the world from 2015 to 2019, Linkedin randomly varied the proportion of weak and strong contacts suggested by its “People You May Know” algorithm — the company’s automated system for recommending new connections to its users. Researchers at LinkedIn, M.I.T., Stanford and Harvard Business School later analyzed aggregate data from the tests in a study published this month in the journal Science.

LinkedIn’s algorithmic experiments may come as a surprise to millions of people because the company did not inform users that the tests were underway.

Tech giants like LinkedIn, the world’s largest professional network, routinely run large-scale experiments in which they try out different versions of app features, web designs and algorithms on different people. The longstanding practice, called A/B testing, is intended to improve consumers’ experiences and keep them engaged, which helps the companies make money through premium membership fees or advertising. Users often have no idea that companies are running the tests on them. (The New York Times uses such tests to assess the wording of headlines and to make decisions about the products and features the company releases.)

But the changes made by LinkedIn are indicative of how such tweaks to widely used algorithms can become social engineering experiments with potentially life-altering consequences for many people. Experts who study the societal impacts of computing said conducting long, large-scale experiments on people that could affect their job prospects, in ways that are invisible to them, raised questions about industry transparency and research oversight.

“The findings suggest that some users had better access to job opportunities or a meaningful difference in access to job opportunities,” said Michael Zimmer, an associate professor of computer science and the director of the Center for Data, Ethics and Society at Marquette University. “These are the kind of long-term consequences that need to be contemplated when we think of the ethics of engaging in this kind of big data research.”

The study in Science tested an influential theory in sociology called “the strength of weak ties,” which maintains that people are more likely to gain employment and other opportunities through arms-length acquaintances than through close friends.

The researchers analyzed how LinkedIn’s algorithmic changes had affected users’ job mobility. They found that relatively weak social ties on LinkedIn proved twice as effective in securing employment as stronger social ties.