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, says a new study.

In experiments conducted 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 in Science and co-authored by researchers at LinkedIn, MIT, Stanford University and Harvard Business School, all in the US.

Tech giants routinely run large-scale experiments in which they try out different 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 companies make money through premium membership fees or advertising. Users often have no idea companies are running the tests on them.

But the changes made by LinkedIn are indicative of how such tweaks can become social engineering experiments with potentially life-altering consequences.

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

The study, published 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 analysed 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 said it had “acted consistently with” the company’s user agreement, privacy policy and member settings. The privacy policy notes that LinkedIn uses personal data for research purposes.

The goal of the research was to “help people at scale,” said Karthik Rajkumar, an applied research scientist at LinkedIn, 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 and the lead author, said LinkedIn’s experiments were an effort to ensure that users had equal access to employment opportunities.

The algorithm analyses data like members’ employment history, job titles and ties to other users. Then it tries to gauge the likelihood that a member will send a friend invite to a suggested new connection as well as the likelihood of that new connection accepting the invite.

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” caused users to form more connections with people with whom they had only weak social ties. Other tweaks caused people to form fewer connections with weak ties.

After the first wave of algorithmic testing, researchers at LinkedIn and MIT hit upon the idea of analysing the outcomes from those experiments to test the theory of the strength of weak ties. The study reported that people who received more recommendations for moderately weak contacts generally applied for and accepted more jobs — results that dovetailed with the weak-tie theory. Weak-tie connections proved most useful for job seekers in digital fields like artificial intelligence, while strong ties proved more useful for employment in industries that relied less on software, the study said.

Aral of MIT said the deeper significance of the study was that it showed the importance of powerful social networking algorithms — not just in amplifying problems like misinformation but also as fundamental indicators of economic conditions like employment and unemployment.

NYTNS