LinkedIn's Hypocrisy
Enterprise Fairness Vs Public disservice
Letter from the editor,
2025 has already been, well, a lot. But one question keeps surfacing as we head into what is a social media detox period for many:
Where are women supposed to go now to be heard, taken seriously, and rewarded for their expertise? Not just performance and self aggrandising posts.
And what about women of colour (or as we like to think of as, women in the rest of the world)?
As The Chief Brief rethinks how serious analysis travels in an AI-first media economy, our reporter Jessica Jurkschat turns the lens on LinkedIn itself. Her reporting examines how platform algorithms, often treated as neutral infrastructure may be systematically narrowing visibility for women and people of colour. But it turns out the LinkedIn algorithm already knows how to be fair. Just not for its public users.
Her’s is not a story about the ‘How-To’s’ of reach or personal branding. It is asking the basic question about how professional opportunity is being constrained, or withdrawn globally. Not just in the proximity of Silicon Valley HQs. And the question most critical raised? Who pays the price when visibility becomes a gatekeeping function?
LinkedIn’s Visibility Collapse
By Jessica Jurkschat
One fact hasn’t been getting nearly enough attention in the recent debates about LinkedIn’s algorithmic bias.
LinkedIn already knows how to build a fair algorithm. It just doesn’t apply it to the public feed.
In its recruitment products, LinkedIn uses what it openly calls fairness-aware ranking. Candidate searches are first ranked by relevance skills, experience and profile data, then re-ranked to correct for representation gaps. In plain terms, the system actively intervenes so under-represented groups are not systematically buried by historical bias baked into industries, seniority and career paths.
LinkedIn has publicly stated that this approach improves fairness outcomes without harming business performance. It is now standard across LinkedIn Recruiter, used by roughly 120,000 enterprise customers.
Now compare that to the public feed, which reaches over 1.2 billion members.
The feed is primarily optimised for engagement and relevance. It rewards signals like activity, industry, seniority, network effects and linguistic patterns.
LinkedIn says it aims for “diversity of sources”, but that means not showing too many posts from the same account, not ensuring fairness in who gets seen.
This is where proxy bias does its work.
When senior roles, high-visibility sectors and dominant communication styles are already skewed male and disproportionately white, optimising for those signals amplifies existing advantage. The system doesn’t need to “see” gender or race to reproduce inequality. It just needs to optimise for proxies closely correlated with both.
That’s why women report sharp drops in reach when posting about female founders or gender equity. Why people of colour see visibility collapse in certain topic areas. And why rewriting posts in more “bro-coded” language or, in one documented case from Megan Cornish, LICSW, changing a gender marker to male resulted in a 400% increase in views in one week.
A recent 90-minute discussion led by Cindy Gallop, Jane Evans, Kalyanna Williams, Ruth M. Dasary Sierra Ph.D., Dorothy Dalton, Robert Baker, Nadine Nembach and Martyn Redstone examined this gap and why it matters well beyond gender.
Visibility on LinkedIn shapes deal flow, hiring, speaking opportunities, credibility and income. When reach disappears, opportunity follows. That’s why algorithmic suppression is not just a platform issue, it’s also an economic one.
For professionals of colour, the effects compound at the intersection of industry, language, seniority and network access.
The message is implicit but clear: adapt to the algorithm, or disappear from the feed.
Here’s what you can do:
Watch the full conversation: https://lnkd.in/esw55MS4
Sign the petition calling for fair visibility: https://lnkd.in/esuAaM5m
Download and share posts from hashtag#FairnessInTheFeed: https://lnkd.in/enJwX2-j
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