\n<\/p>\n
One day in November, a product strategist we\u2019ll call Michelle (not her real name), logged into her LinkedIn account and switched her gender to male. She also changed her name to Michael, she told TechCrunch.\u00a0<\/p>\n
She was partaking in an experiment called #WearthePants where women tested<\/a> the hypothesis that LinkedIn\u2019s new algorithm was biased against women.\u00a0<\/p>\n For months, some heavy LinkedIn<\/a> users complained about seeing drops in engagement and impressions on the career-oriented social network. This came after the company\u2019s vice president of engineering, Tim Jurka, said in August that the platform<\/a> had \u201cmore recently\u201d implemented LLMs to help surface content useful to users.\u00a0<\/p>\n Michelle (whose identity is known to TechCrunch) was suspicious about the changes because she has more than 10,000 followers and ghostwrites posts for her husband, who has only around 2,000. Yet she and her husband tend to get around the same number of post impressions, she said, despite her larger following.\u00a0<\/p>\n \u201cThe only significant variable was gender,\u201d she said.\u00a0<\/p>\n Marilynn Joyner, a founder, also changed her profile gender. She\u2019s been posting on LinkedIn consistently for two years and noticed in the last few months that her posts\u2019 visibility declined. \u201cI changed my gender on my profile from female to male, and my impressions jumped 238% within a day,\u201d she told TechCrunch.<\/p>\n Megan Cornish\u00a0reported\u00a0similar results,\u00a0as\u00a0did\u00a0Rosie Taylor,\u00a0Jessica Doyle\u00a0Mekkes, Abby Nydam,\u00a0Felicity Menzies, Lucy Ferguson,\u00a0and\u00a0so on<\/a>.\u00a0\u00a0<\/p>\n Techcrunch event<\/p>\n \n\t\t\t\t\t\t\t\t\tSan Francisco<\/span> LinkedIn said that<\/a> its \u201calgorithm and AI systems\u00a0do not use demographic\u00a0information such as age, race, or gender as a signal to determine the visibility of content, profile, or posts in the Feed\u201d and that \u201ca\u00a0side-by-side snapshot of your own feed updates that are not perfectly representative, or equal in reach, do not automatically imply unfair treatment or bias\u201d within the Feed.\u00a0<\/p>\n Social algorithm\u00a0experts\u00a0agree that explicit sexism may not have been a\u00a0cause,\u00a0although implicit bias may be at work.\u00a0\u00a0<\/p>\n Platforms are\u00a0\u201can intricate symphony of algorithms that pull specific mathematical and social levers, simultaneously and constantly,\u201d\u00a0Brandeis Marshall<\/a>, a data ethics consultant, told TechCrunch.\u00a0\u00a0<\/p>\n \u201cThe\u00a0changing\u00a0of one\u2019s profile photo and name is just one such lever,\u201d she\u00a0said, adding that the algorithm is\u00a0also influenced by, for example, how a user has and currently interacts with other content.\u00a0\u00a0<\/p>\n \u201cWhat we\u00a0don\u2019t\u00a0know of\u00a0is\u00a0all the other levers that make this algorithm prioritize one\u00a0person\u2019s\u00a0content over another. This is a more complicated problem than people assume,\u201d Marshall said.\u00a0<\/p>\n The #WearthePants<\/a> experiment began with two entrepreneurs \u2014 Cindy Gallop and Jane Evans.<\/p>\n They asked two men to make and post the same content as them, curious to know if gender was the reason so many women were feeling a dip in engagement. Gallop and Evans both have sizable followings<\/a> \u2014 more than 150,000 combined compared to the two men who had around 9,400 at the time.\u00a0<\/p>\n Gallop reported that her post reached only 801 people, while the man who posted<\/a> the exact same content reached 10,408 people, more than 100% of his followers. Other women then took part. Some, like Joyner, who uses LinkedIn to market her business, became concerned.<\/p>\n \u201cI\u2019d really love to see LinkedIn take accountability for any bias that may exist within its algorithm,\u201d Joyner said.\u00a0<\/p>\n But LinkedIn, like other LLM-dependent search and social media platforms, offers scant details on how content-picking models were trained.<\/p>\n Marshall said that most of these platforms \u201cinnately have embedded a white, male, Western-centric viewpoint\u201d due to who trained the models. Researchers find evidence of human biases like sexism and racism<\/a> in popular LLM models because the models are trained on human-generated content, and humans are often directly involved in post-training<\/a> or reinforcement learning.\u00a0<\/p>\n Still, how any individual company implements its AI systems is shrouded in the secrecy of the algorithmic black box.\u00a0<\/p>\n LinkedIn says that the #WearthePants experiment could not have demonstrated gender bias against women. Jurka\u2019s August statement said \u2014 and LinkedIn\u2019s Head of Responsible AI and Governance, Sakshi Jain, reiterated in another post<\/a> in November \u2014 that its systems are not using demographic information as a signal for visibility.\u00a0<\/p>\n Instead, LinkedIn told TechCrunch that it tests millions of posts to connect users to opportunities. It said demographic data is used only for such testing, like seeing if posts \u201cfrom different creators compete on equal footing and that the scrolling experience, what you see in the feed, is consistent across audiences,\u201d the company told TechCrunch.<\/p>\n LinkedIn has been noted for researching and adjusting<\/a> its algorithm to try to provide a less biased<\/a> experience for users.<\/p>\n It\u2019s the unknown variables, Marshall said, that probably explain why some women saw increased impressions after changing their profile gender to male. Partaking in a viral trend, for example, can lead to an engagement boost; some accounts were posting for the first time in a long time, and the algorithm could have possibly rewarded them for doing so.\u00a0<\/p>\n Tone and writing style might also play a part. Michelle, for example, said the week she posted as \u201cMichael,\u201d she adjusted her tone slightly, writing in a more simplistic, direct style, as she does for her husband. That\u2019s when she said impressions jumped 200% and engagements rose 27%.<\/p>\n She concluded the system was not \u201cexplicitly sexist,\u201d but seemed to deem communication styles commonly associated with women \u201ca proxy for lower value.\u201d\u00a0<\/p>\n Stereotypical male writing styles are believed to be more concise<\/a>, while the writing style stereotypes for women<\/a> are imagined to be softer and more emotional. If an LLM is trained to boost writing that complies with male stereotypes, that\u2019s a subtle, implicit bias. And as we previously reported<\/a>, researchers have determined that most LLMs are riddled with them.<\/p>\n Sarah Dean, an assistant professor of computer science at Cornell, said that platforms like LinkedIn often use entire profiles, in addition to user behavior, when determining content to boost. That includes jobs on a user\u2019s profile and the type of content they usually engage with.<\/p>\n \u201cSomeone\u2019s demographics can affect \u2018both sides\u2019 of the algorithm \u2014 what they see and who sees what they post,\u201d Dean said.\u00a0<\/p>\n LinkedIn told TechCrunch that its AI systems look at hundreds of signals to determine what is pushed to a user, including insights from a person\u2019s profile, network, and activity.\u00a0<\/p>\n \u201cWe run ongoing tests to understand what helps people find the most relevant, timely content for their careers,\u201d the spokesperson said. \u201cMember behavior also shapes the feed, what people click, save, and engage with changes daily, and what formats they like or don\u2019t like. This behavior also naturally shapes what shows up in feeds alongside any updates from us.\u201d<\/p>\n
\n\t\t\t\t\t\t\t\t\t\t\t\t\t|<\/span>
\n\t\t\t\t\t\t\t\t\t\t\t\t\tOctober 13-15, 2026<\/span>\n\t\t\t\t\t\t\t<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/div>\nBro-coded<\/h2>\n