LinkedIn Gender Bias, AI Patterns, and What Visibility Really Means

Have you ever wondered why some voices travel farther online while others stay buried at the bottom of the feed? The more I study career systems and digital platforms, the more I realise the field is not equal. Not in hiring and not even on social media. And now there is data showing it.

Recent studies on algorithmic bias found that posts from women receive far less visibility than posts from men. The World Economic Forum reported that women get up to 30 percent less reach on professional platforms. A study by Cornell University found that online algorithms consistently amplify white coded language more than language patterns linked to people of colour. They shape who gets seen, who gets heard and who gets picked for opportunities.

And now LinkedIn is part of the conversation. BBC and The Guardian have reported that some women who changed their gender setting to male saw higher post views. Others rewrote their content using male coded language and saw impressions rise. While women of colour who did the same saw impressions go down. So the system is not only reacting to gender. It is reacting to intersectionality.

This trend made me test something myself. I took my real bio, the actual story I tell about my work and my lived experience, and I asked ChatGPT to rewrite it in three versions. White female coded, POC women coded and South Asian women coded. I kept the same structure and asked the model to explain every change.

Across these versions, the model also explained the deeper patterns behind each rewrite. It said white women can lead with story and still be seen as credible. POC women need a mix of credentials and strategy to be read as leaders. South Asian women need stronger authority signals, data, expertise and performance proof. Warmth from South Asian women is often misread as passivity. Warmth from white women is often read as leadership confidence.

These are patterns the model learned from global data. And these patterns are being picked up by platforms like LinkedIn whether we like it or not. This is proxy bias.

DATA USE AND VISIBILITY

This brings me to something that has been on my mind. What happens when we declare who we are on platforms. When we choose our gender, our identity, our demographic or even our pronouns. How is our data being used. They say it is for insights and research, but who really knows what is happening behind the scenes. Who gets visibility. Who gets pushed down. And how does someone get to the top of a search list.

AI IN HIRING

And it does not stop at social media. In one of my earlier episodes I talked about AI interview tools . One way video interviews. Automated scoring systems. Tools that judge your verbal communication, your accent, your pacing and even your pauses. Who are these tools coded for. Who fits the template of confidence. Who gets misread. These questions matter because these systems now screen tens of thousands of candidates before a human ever sees them.

THE BIG PROBLEM

So when governments invest over one billion dollars into AI and quantum computing, as the Canadian budget just announced, we have to ask a simple question. Who is auditing these algorithms. Who is checking the patterns. Who is holding these tools accountable when they quietly punish underrepresented communities.

If you are looking for an authentic keynote speaker in Canada or globally who speaks on career development, workplace diversity, AI biases, and the immigrant journey, book Sweta Regmi for your next event.

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