International Women’s Day: Bias in Tech Work and What You Can Do.

Our brains are shaped by our upbringing, our culture, the media we consumed, the teams we have worked on, and the patterns we have seen reinforced over years. Your brain is basically a very fast pattern matching machine. This is great when we need to figure out if a patch of shadow is a normal shadow or a very large animal that wants to eat us.It’s less helpful when we’re in modern society.

This can be a hard thought to sit with. I really do like to believe that most people don’t get up in the morning and think “bwaha, I’m so excited to exhibit biases against people due to their gender, nationality, sexual orientation, and sports team affiliation”. (I know people definitely do this but they’re probably not reading this article because they don’t care to do better)

International Women’s Day is a good moment to talk about some techniques to improve things for the people you work with.

What Bias Actually Looks Like in Tech

Bias in technical environments rarely announces itself. It hides behind the warm, comforting lie of meritocracy. “If you are good at your job, you will succeed”

I regret to inform you that tech is still full of humans, and humans are inconsistent.

If any of these feel familiar, you’ve seen bias in the wild:

• The loudest voice in architecture discussions gets treated as the most competent
• Women and underrepresented engineers get more feedback on “communication style” than on technical depth
• Someone is considered strong because they’re “sound” and good craic to go for a pint withThe same engineers keep getting the high visibility projects
• The same people quietly pick up the team’s glue work

Glue work is the unglamorous but essential stuff that keeps teams from quietly falling apart.

You know the list:

• Writing documentation
• Onboarding new joiners
• Chasing cross-team dependencies
• Cleaning up processes
• Running retros
• Unblocking everyone else

Across many orgs, women and underrepresented engineers end up doing more than their fair share of this. You know the person - they always put their hands up when the boss asks for a volunteer. A lot of the times the same people are pushed/asked/voluntold to do this when nobody on the team puts their hand up, even if they’re less suited or have less capacity, compared to the others (usually men) on the same teamGlue work rarely carries the same promotion weight as shiny technical ownership. So the people making the team function smoothly are often the same people being told later:

“You’re such a team player, but we need to see more impact.”

You cannot “team player” your way into a promotion if the system only rewards visible hero work. You don’t get paid for glue work even though it takes time away from your technical work.

If your promotion and performance decisions rely heavily on gut feel, bias is already in the room.

Impactful Impact

Engineering teams understand guardrails everywhere else. You would not ship critical code based on “feels right to me.” (right?! Padame.jpg)

One of the highest impact fixes is making evaluation criteria painfully explicit.

Because when someone is told to:

• “Show more ownership”
• “Be more senior”
• “Have more impact”

…those phrases mean whatever the listener is afraid they mean.

Clear criteria leads to:

• more consistent feedback
• fewer personality contests
• less scope for unconscious nonsense

Start treating glue work like it actually matters.

High functioning teams tend to:

• make glue work visible in performance conversations
• check whether people actually want to keep doing it
• recognise team impact alongside individual wins
• rotate this work when the same names keep appearing
• make glue work part of promotion packages

If the work is essential, it should count. Radical concept, I know. I am a maverick and cannot be contained.

Watch Your Meetings. The Patterns Are There.

If you want to see bias in real time, watch a few team meetings with your eyes properly open.

Look for:

• who gets interrupted
• whose ideas get repeated and credited to someone else
• who gets grilled with ten follow-up questions
• who says something useful and gets complete silence

Senior engineers and tech leads, especially white men, often have disproportionate influence in these moments. If that’s you, you have more leverage than you think.

Small interventions work:

• “Priya mentioned that earlier.”
• “Sorry Simon, Ella was just making a point.”
• “oh, you mean what Ashley was saying earlier?”
• explicitly credit ideas in notes

It takes five seconds and you can do it today.

Structure Beats Volume

Another practical fix is adding more structure to how ideas are evaluated.

For example:

• Problem: What are we solving?
• Purpose: Why does it matter?
• Frequency: Who is affected and how often?
• Edge cases: What weird stuff will break?

This is just good engineering discipline. It reduces the chance that the loudest suggestion wins by default. This is probably a good idea anyway - people can structure their ideas a little instead of going “why don’t we do this?” (I am personally very guilty of having ideas and no follow up)

Project Exposure Is Career Fuel

Careers in tech are heavily shaped by project exposure. If the same names always get the interesting work, you are reinforcing existing patterns whether you mean to or not.

Healthy teams periodically review:

• who is getting stretch work
• who is stuck in maintenance or glue loops
• who is actually visible to leadership

Equity of opportunity drives equity of outcomes.

If You Have Influence, Use It Properly

Privilege in tech shows up in lots of ways. Title helps, but so does reputation, tenure, or simply being part of the majority group in the room.

Using that leverage is not about guilt. It is about impact.

In practice, it looks very normal:

• amplifying a point that got talked over
• recommending someone new for visible work
• giving precise public credit
• pushing back on vague negative feedback

In engineering cultures, informal reputation carries enormous weight. One well placed comment from a respected person can materially shift how someone is perceived.

Check Your Own Bias

Managing your own bias is the same muscle you already use elsewhere. Treat strong first impressions like hypotheses, not conclusions.

Ask yourself:

• What specific behaviour triggered this reaction?
• Do I actually have enough data?
• Would I interpret this differently from someone else?

Be very suspicious of the phrase “culture fit.” In many teams it quietly means “feels familiar.”

A better question is whether someone demonstrates the behaviours the team actually values, with evidence to back it up.

Conclusions, if any.

Bias is not going away. If anything, poorly implemented AI is about to scale some of it at impressive speed. If the data AI pulls from is biased, what is an innocent LLM to do?There’s about 900 things I didn’t mention here - I was trying to write an article, not a book (or manifesto). I could talk about salary disparity, how people on work permits are given crappy treatment because companies know they can’t leave without leaving the country. How “do they have side-projects?” negatively impacts working mothers who might love to do a fun project but have the second shift after work instead of making a MVP. I can’t list everything or fix everything but I can give you a way to make an impact right now.

The teams that will pull ahead are the ones treating fairness and inclusion the same way they treat reliability and security.

You can start today.

If you have a bit more power in the room, use it to make the room better.

Disclaimer: I used some AI to write this - I wrote the article and asked chatgpt to tidy it up. I tend towards stream of consciousness writing which isn’t always the easiest to read.)

If you’re comfortable, share your experiences with us, the more we know about the issues people face with confronting bias, the better. If you want to talk more about DEI, connect with me! And if you want to hire lots of amazing people, definitely check out ninedots.io.