LinkedIn ran experiments on more than 20 million users for five years that, while intended to improve how the platform worked for members, could have affected some people’s livelihoods, according to a 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. The tests were detailed in a study published last month in the journal Science and co-authored by researchers at LinkedIn, the Massachusetts Institute of Technology, Stanford University and Harvard Business School.
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 experiments in which they try out versions of app features, web designs and algorithms on people. The long-standing 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.
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 effects 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.
The study in Science tested an influential theory in sociology called “the strength of weak ties,” which maintains 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.
LinkedIn, which is owned by Microsoft, did not directly answer a question about how the company had considered the potential long-term consequences of its experiments on users’ employment and economic status. But the company said the research had not disproportionately advantaged some users.
The goal of the research was to “help people at scale,” said Karthik Rajkumar, an applied research scientist at LinkedIn who was one of the study’s co-authors. “No one was put at a disadvantage to find a job.”
Sinan Aral, a management and data science professor at MIT who was the lead author of the study, said LinkedIn’s experiments were an effort to ensure users had equal access to employment opportunities.
The LinkedIn professional networking experiments were designed by LinkedIn as part of the company’s continuing efforts to improve the relevance of its “People You May Know” algorithm, which suggests new connections to members.
The algorithm analyzes data such as members’ employment history, job titles and ties to other users. Then it tries to gauge the likelihood that a LinkedIn member will send a friend invite to a suggested new connection and the likelihood of that new connection accepting the invite.
For the experiments, LinkedIn adjusted its algorithm to randomly vary the prevalence of strong and weak ties that the system recommended. The first wave of tests, conducted in 2015, “had over 4 million experimental subjects,” the study reported. The second wave of tests, conducted in 2019, involved more than 16 million people.
During the tests, people who clicked on the “People You May Know” tool and looked at recommendations were assigned to different algorithmic paths. Some of those “treatment variants,” as the study called them, caused LinkedIn users to form more connections to people with whom they had only weak social ties. Other tweaks caused people to form fewer connections with weak ties.