LinkedIn ran a secret social experiment. It may have cost some people a job

In a statement, LinkedIn said that during the study it had “acted consistently with” the company’s user agreement, privacy policy and member settings. The privacy policy notes that LinkedIn uses members’ personal data for research purposes. The statement added that the company used the latest, “noninvasive” social science techniques to answer important research questions “without any experimentation on members.”

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 that users had equal access to employment opportunities.

“To do an experiment on 20 million people and to then roll out a better algorithm for everyone’s jobs prospects as a result of the knowledge that you learn from that is what they are trying to do,” Aral said, “rather than anointing some people to have social mobility and others to not.” (Aral has conducted data analysis for The New York Times, and he received a research fellowship grant from Microsoft in 2010.)

“No one was put at a disadvantage to find a job.”

Karthik Rajkumar, an applied research scientist at LinkedIn

Experiments on users by big internet companies have a checkered history. Eight years ago, a Facebook study describing how the social network had quietly manipulated what posts appeared in users’ News Feeds in order to analyse the spread of negative and positive emotions on its platform was published. The weeklong experiment, conducted on 689,003 users, quickly generated a backlash.

The LinkedIn professional networking experiments were different in intent, scope and scale. They 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 analyses data like 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 as well as 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.

Whether most LinkedIn members understand that they could be subject to experiments that may affect their job opportunities is unknown.

LinkedIn’s privacy policy says the company may “use the personal data available to us” to research “workplace trends, such as jobs availability and skills needed for these jobs.” Its policy for outside researchers seeking to analyse company data clearly states that those researchers will not be able to “experiment or perform tests on our members.”

But neither policy explicitly informs consumers that LinkedIn itself may experiment or perform tests on its members.

In a statement, LinkedIn said, “We are transparent with our members through our research section of our user agreement.”

In an editorial statement, Science said, “It was our understanding, and that of the reviewers, that the experiments undertaken by LinkedIn operated under the guidelines of their user agreements.”

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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. Although the decades-old theory had become a cornerstone of social science, it had not been rigorously proved in a large-scale prospective trial that randomly assigned people to social connections of different strengths.

The outside researchers analysed aggregate data from LinkedIn. 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.

The 20 million users involved in LinkedIn’s experiments created more than 2 billion new social connections and completed more than 70 million job applications that led to 600,000 new jobs, the study reported. 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.

LinkedIn said it had applied the findings about weak ties to several features, including a new tool that notifies members when a first- or second-degree connection is hiring. But the company has not made study-related changes to its “People You May Know” feature.

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.

Catherine Flick, a senior researcher in computing and social responsibility at De Montfort University in Leicester, England, described the study as more of a corporate marketing exercise.

“The study has an inherent bias,” Flick said. “It shows that, if you want to get more jobs, you should be on LinkedIn more.“