Why Should Boys Have All the Fun?
A bunch of women have started doing something that feels equal parts hilarious and heartbreaking: they’re switching their gender to “male” on LinkedIn (and in some cases, on resumes / job applications) to see if the internet and the hiring machine treats them differently.
And the reports are…telling.
Across viral “gender-switch” tests, women say the same content performs dramatically better when their profile is set to male or when they “bro-code” their writing, even though LinkedIn has publicly stated that its Feed ranking doesn’t use demographic signals like gender, age, or race.
So, what’s going on?
Whether or not gender is directly used as an algorithmic input, systems can still produce gendered outcomes because they’re trained on history.
And history… has a favorite protagonist (spoiler alert, it’s a man).
This isn’t about “women being dramatic about social media impressions.” This is a symptom. A signal flare. A giant neon sign that says “Our professional systems, platforms, hiring funnels, and AI tools, still reward ‘male-coded’ norms.”
This isn’t about “women being dramatic about social media impressions.” This is a symptom. A signal flare. A giant neon sign that says “Our professional systems, platforms, hiring funnels, and AI tools, still reward ‘male-coded’ norms.”
The The “male toggle” trend isn’t a hack. It’s a diagnosis.
Women change gender → visibility goes up (as per widely shared informal tests and reporting).
Women change language → visibility goes up (“more assertive,” “more decisive,” fewer “softeners,” fewer community cues).
Women change identifiers on resumes → callbacks shift (name, gender cues, age cues).
Even if individual experiments are imperfect (they often are), they’re pointing at a reality many women in India already know in their bones: The professional world still has a “default setting.” And it’s bro-coded as heck.
India context
In India, the “why” isn’t subtle. It’s structural.
1) Our participation numbers are improving-yet the pipeline is still fragile
Government PLFS reporting shows female labour force participation has risen over recent years (with rural participation doing a lot of the heavy lifting). But here’s the uncomfortable part: if women have to fight the system for basic visibility and credibility, those numbers don’t automatically translate into:
better careers,
safer workplaces,
fairer promotions,
or leadership representation.
Participation isn’t the same as power.
2) Hiring discrimination doesn’t always look like “no women allowed”
It often looks like:
“She might get married soon.”
“Will she be comfortable travelling?”
“Culture fit.”
“Night shifts?”
“Client-facing presence.”
And the research is catching up to what women have been saying forever. For example, an online field experiment in Indian metro cities found gendered differences in employer response when marital status cues are present, even for “gender-neutral” HR-domain jobs. (ISID)
So when women “mask” gender online, it’s not because they want to cosplay as a man.It’s because they’re trying to dodge a bias they can’t afford.
Leaders need to stop treating this like a “future problem.”
Watch The Coded Bias (Documentary) as an exploration of tech that literally sees the world through bias.
Platforms can deny using gender while still amplifying gendered outcomes
LinkedIn says demographic info isn’t used as a signal for Feed visibility. But visibility can still skew because:
training data reflects historically male-dominant professional norms,
engagement patterns reflect social bias (who people choose to boost),
“authority” is culturally coded as masculine,
and models optimize for what historically performed.
So you get a system that says “gender-blind” while behaving “gender-shaped.”
AI hiring tools can reproduce discrimination at scale
We’ve already seen how this goes: Reuters reported on Amazon scrapping an internal recruiting tool after it showed bias against women. And research + policy commentary keep warning that AI-enabled recruitment can encode and amplify discrimination, especially when it’s trained on biased historical hiring decisions. UN Women has also warned that AI systems can amplify gender inequality when trained on biased data and deployed without accountability.
Now connect the dots to India:
Many companies rely on dashboards, screening tools, ATS filters, keyword rankers.
Leaders trust “data-led hiring” without asking what the data learned from.
If women are underrepresented in certain roles historically, the system learns: women don’t belong there.
That’s how bias becomes part of the model.
The most dangerous part: biased data becomes “truth” inside leadership rooms
In India, a lot of workplace decision-making runs on:
“pipeline numbers”
“quality of applicants”
“offer drop-offs”
“performance ratings”
“culture fit scores”
If your inputs are biased (who applies, who gets seen, who gets shortlisted), your shiny leadership dashboards become a high-gloss way of saying: “The system is working.”
No, babe. The system is performing.
And when women respond by “male-switching” to survive the algorithm, that’s not empowerment. That's an adaptation to a rigged game.
So what should Indian leaders and HR teams do, practically?
Not performative webinars. Not “women’s day panels.” Actual systems overhaul work.
1) Audit your funnel like you audit revenue
Disaggregate at every step:
source → shortlist → interview → offer → join → first-year retention → promotion velocity
2) Treat AI like a junior recruiter with zero context (because it is)
If you use AI in screening/interviewing:
demand explainability,
test outcomes for gender skews,
ensure human oversight,
and run bias checks on both model outputs and the training labels you’re feeding it.
3) Fix the “authority aesthetic” inside your culture
If women are rewarded only when they write/speak like men, you don’t have meritocracy.You have a style guide. And it’s giving major dude/bro energy.
4) Make anonymisation and structured evaluation real (and monitored)
Blind review can help in early stages when paired with structured scoring, not vibes. And you still need monitoring, because bias simply relocates to later rounds if culture doesn’t change.
5) Stop outsourcing accountability to tools
Your ATS didn’t reject her.Your culture did, through the tool.
THE Sassy Takeaway
Women changing their gender on LinkedIn or stripping identity cues from applications isn’t a quirky trend. It’s a warning sign telling us that:
visibility is still gendered,
authority is still coded,
and “data-driven decisions” can be biased at the source-especially when AI is involved.
So, why should boys have all the fun? They shouldn’t.
But women also shouldn’t have to become boys (digitally, linguistically, or professionally) to get treated like professionals.That’s the actual HR problem. And it’s fixable…iif we stop calling it a “social media drama” and start calling it what it is: a systems failure with a Wi-Fi connection.
RESOURCE HUB
Invisible Women: Exposing Data Bias by Caroline Criado Perez - excellent context on why big data fails women.
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