LinkedIn, which is owned by Microsoft, did not directly answer a question about how the company had accounted for the potential long-term consequences of its experiments on users’ employment and economic status. However, the company said the research did not bring some users a disproportionate benefit.
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. “Nobody was disadvantaged to find a job.”
Sinan Aral, a management and data science professor at MIT and the study’s lead author, said LinkedIn’s experiments are an attempt to ensure users have equal access to job opportunities.
“Doing an experiment with 20 million people and then rolling out a better algorithm for everyone’s job prospects as a result of the knowledge you learn from that is what they’re trying to do,” Aral said, “rather than anointing some people on social mobility.” to have and others not.” (Aral has done data analysis for The New York Times and received a research grant from Microsoft in 2010.)
“Nobody was disadvantaged to find a job.”
Karthik Rajkumar, an applied research scholar at LinkedIn
User experiments by large Internet companies have a checkered history. Eight years ago, a Facebook study was published describing how the social network had silently manipulated the posts in users’ news feeds in order to analyze the spread of negative and positive emotions on its platform. The week-long experiment, conducted on 689,003 users, quickly sparked a backlash.
LinkedIn professional networking experiments varied in intent, scope, and scope. They were developed by LinkedIn as part of the company’s ongoing effort 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 connections to other users. It then attempts to estimate the likelihood that a LinkedIn member will send a friend invite to a proposed new connection, and the likelihood that that new connection will accept the invitation.
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 testing, conducted in 2015, “had over 4 million subjects,” the study reported. The second wave of testing, conducted in 2019, involved more than 16 million people.
During testing, people who clicked the People You May Know tool and viewed recommendations were matched to different algorithmic paths. Some of these “treatment variations,” as the study called them, caused LinkedIn users to form more connections with people with whom they had weak social bonds. Other tweaks resulted in people forming fewer connections with weak bonds.
Whether most LinkedIn members understand that they may be subject to experiments that could affect their employment opportunities is unknown.
But none of the policies explicitly inform consumers that LinkedIn itself may experiment or conduct tests on its members.
In a statement, LinkedIn said, “We are transparent to our members through our research section of our User Agreement.”
In an editorial statement, Science said, “To our understanding and that of the reviewers, the experiments conducted by LinkedIn worked according to the guidelines of their user agreements.”
After the first wave of algorithmic testing, researchers from LinkedIn and MIT came up with the idea of analyzing the results of these experiments to test the weak bond strength theory. Although the decades-old theory had become a cornerstone of the social sciences, it had not been rigorously proven in a large-scale prospective experiment that randomly assigned people to social connections of different strengths.
The external researchers analyzed aggregated data from LinkedIn. The study reported that people who received more referrals for moderately weak attachments generally applied for and accepted more jobs — findings consistent with weak attachment theory.
The 20 million users involved in LinkedIn’s experiments created more than 2 billion new social connections and filled out more than 70 million applications that resulted in 600,000 new jobs, the study reported. Weak ties proved most useful for job seekers in digital fields like artificial intelligence, while strong ties proved more useful for employment in industries less reliant on software, the study found.
LinkedIn said it applied the weak connection learnings to several features, including a new tool that notifies members when a first- or second-degree connection drops. However, the company hasn’t made any study-related changes to its People You May Know feature.
MIT’s Aral said the deeper meaning of the study is that it shows the importance of powerful social networking algorithms – not only 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 more as a marketing exercise for companies.
“The study has an inherent bias,” Flick said. “It shows that if you want to get more jobs, you should be on LinkedIn more.”
https://www.smh.com.au/business/workplace/linkedin-ran-social-experiments-on-20-million-users-over-five-years-20220925-p5bkry.html?ref=rss&utm_medium=rss&utm_source=rss_business LinkedIn conducted social experiments with 20 million users over five years