One day in November, a product strategist we’ll call Michelle (not her real name), logged into her LinkedIn account and changed her gender to male. She also changed her name to Michael, she told TechCrunch.
He participated in an experiment called #WearthePants where women tested the assumption that LinkedIn’s new algorithm was biased against women.
For months, some heavy LinkedIn Users complained of seeing a drop in engagement and impressions on the career-oriented social network. This was after the company’s vice president of engineering, Tim Jurka, said in August that the platform had “more recently” implemented LLM to help display useful content to users.
Michelle (whose identity is known to TechCrunch) was suspicious of the changes because she has more than 10,000 followers and ghostwrites posts for her husband, who only has about 2,000. However, she and her husband tend to have the same number of post impressions, she said, despite her larger following.
“The only significant variable was gender,” he said.
Marilynn Joyner, founder, also changed the gender of her profile. She has been posting consistently on LinkedIn for two years and has noticed over the past few months that the visibility of her posts has decreased. “I changed the gender of my profile from female to male and my impressions increased by 238% in one day,” he told TechCrunch.
Megan Cornish reported similar results, as did Rosie Taylor, Jessica Doyle Mekkes, Abby Nydam, Felicity Menzies, Lucy Ferguson and so on.
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LinkedIn he said that “Algorithm and artificial intelligence systems do not use demographic information such as age, race or gender as a signal to determine the visibility of content, profile or posts in the feed” and that “a snapshot of your own feed updates that is not fully representative or equal does not automatically imply unfair treatment or bias in the feed.”
Social algorithm experts agree that overt sexism may not have been a cause, although implicit bias may be at work.
Platforms are “a complex arrangement of algorithms that pull specific mathematical and social levers, simultaneously and continuously.” Brandeis Marshalldata ethics consultant, told TechCrunch.
“Changing one’s profile picture and name is just one such driver,” he said, adding that the algorithm is also influenced by, for example, how a user currently has and interacts with other content.
“What we don’t know are all the other drivers that make this algorithm prioritize one person’s content over another. This is a more complex problem than people realize,” Marshall said.
Bro-coded
THE #WearthePants The experiment started with two entrepreneurs – Cindy Gallop and Jane Evans.
They asked two men to create and post the same content as them, curious to find out if gender was the reason so many women felt a dip in engagement. Gallop and Evans both they have a large following — more than 150,000 combined compared to the two men who had about 9,400 at the time.
Gallop reported that her post reached only 801 people, while the man who posted the exact same content reached 10,408 people, over 100% of his followers. Then other women joined. Some, like Joyner, who uses LinkedIn to promote her business, were concerned.
“I would love to see LinkedIn take responsibility for any bias that may exist in their algorithm,” Joyner said.
However, LinkedIn, like other LLM-dependent search and social media platforms, offers little detail on how to train content selection models.
Marshall said most of these platforms “inherently have embedded a white, male, Western-centric view” because of who trained the models. Researchers find elements of human biases such as sexism and racism in popular LLM models because models are trained on human-generated content and humans are often directly involved in post-training or reinforcement learning.
However, how an individual company implements its AI systems is shrouded in algorithmic black-box secrecy.
LinkedIn says the #WearthePants experiment could not have demonstrated gender bias against women. Jurka’s statement in August said — and LinkedIn’s Head of Responsible Artificial Intelligence and Governance, Sakshi Jain, he repeated in another post in November — that its systems do not use demographic information as a signal for visibility.
Instead, LinkedIn told TechCrunch that it tests millions of posts to connect users with opportunities. It said demographic data is only used for such tests as to see if posts “from different creators are competing on a level playing field and that the scrolling experience, what you see in the stream, is consistent across audiences,” the company told TechCrunch.
LinkedIn has been noted for research and adaptation its algorithm to try to provide a less biased experience for users.
It’s the unknown variables, Marshall said, that likely explain why some women saw increased impressions after changing their profile gender to male. Participating in a viral trend, for example, can lead to a boost in engagement. Some accounts were posting for the first time in a long time and the algorithm could have possibly rewarded them for that.
Tone and writing style can also play a role. Michelle, for example, said the week she posted as “Michael,” she adjusted her tone slightly, writing in a more simplistic, direct style, as she does for her husband. That’s when he said impressions were up 200% and engagements were up 27%.
She concluded that the system was not “overtly sexist” but appeared to consider communication styles commonly associated with women “a proxy for lower value”.
Stereotypical male Writing styles are believed to be more concisewhile the stereotypical writing styles for women they are imagined to be softer and more emotional. If an LLM is trained to reinforce writing that conforms to male stereotypes, this is a subtle, implicit bias. And as we mentioned earlier, researchers have found that most LLMs are full of them.
Sarah Dean, an assistant professor of computer science at Cornell, said platforms like LinkedIn often use entire profiles, in addition to user behavior, when determining content to boost. This includes tasks on a user’s profile and the type of content they typically interact with.
“One’s demographics can affect ‘both sides’ of the algorithm — what they see and who sees what they post,” Dean said.
LinkedIn told TechCrunch that its AI systems look at hundreds of signals to determine what to promote to a user, including information from a person’s profile, network and activity.
“We’re constantly testing to understand what helps people find the most relevant, timely content for their careers,” the spokesperson said. “Member behavior also shapes the feed, the changes users make, save and interact with daily, and what formats they like or dislike. This behavior also naturally shapes what appears in the feeds along with any updates from us.”
Chad Johnson, a LinkedIn sales professional, is described changes such as unfavorites, comments and reposts. The LLM system “no longer cares how often you post or what time of day,” Johnson wrote in a post. “He cares if your writing shows understanding, clarity, and value.”
All of this makes it difficult to determine the true cause of any #WearthePants results.
People just dislike algo
However, it seems that many people, across both genders, either don’t like or don’t understand LinkedIn’s new algorithm — whatever it is.
Shailvi Wakhulu, a data scientist, told TechCrunch that she averaged at least one post a day for five years and used to see thousands of impressions. Now she and her husband are lucky enough to see a few hundred. “It’s discouraging for content creators with large loyal followings,” he said.
One man told TechCrunch that he’s seen about a 50% drop in engagement in recent months. However, another man said he saw impression posts and reach increase more than 100% over a similar period of time. “That’s largely because I’m writing about specific topics for specific audiences, which the new algorithm rewards,” he told TechCrunch, adding that his clients are seeing a similar increase.
But in Marshall’s experience, she, who is Black, believes that posts about her experiences perform worse than posts related to her race. “If black women only have interactions when they’re talking about black women, but not when they’re talking about their particular expertise, then that’s a bias,” she said.
The researcher, Dean, believes the algorithm may simply be amplifying “whatever signals are already there.” It could reward certain posts, not because of the demographic of the author, but because there is more of a history of responding to them across the platform. While Marshall may have stumbled into another area of implicit bias, her anecdotal evidence isn’t enough to determine it for sure.
LinkedIn offered some insight into what’s working well now. The company said its user base has grown and as a result, posts are up 15% year-over-year, while comments are up 24% year-over-year. “This means more competition in streaming,” the company said. Posts about professional knowledge and career lessons, industry news and analysis, and educational or informational content about work, business and the economy do well, he said.
If anything, people are just confused. “I want transparency,” Michelle said.
However, since content selection algorithms are always closely guarded by their companies and transparency can lead to their game, this is a big question. It is something that is unlikely to ever be satisfied.
