Category: Women in tech

Four episodes of sexism at tech community events, and how Sal came out of them (eventually) positive

This is a fantastic post by Sal Freudenberg, describing various experiences of sexism that women may have at tech events, and how they can react / what they can do about it.

Agile in the Wild

It is a sad but undeniable fact that our industry remains rife with gender issues. Coming through any form of sexist encounter is incredibly difficult and I can only be grateful that mine were minor enough, and my general situation and support framework strong enough, for me to come through them largely positive about remaining in tech. I tell my stories here as just some examples of situations that happened to me and that I am sure happen to women in technology each and every day. To show how inadvertently harmful they can be, how absolutely normal it is to be upset by them, how they add up day by day, year by year to make us feel marginalized and unwelcome, and to suggest some ways of dealing with them. I also want to show the importance of having a Code of Conduct at events, both to indicate what is…

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Advice for women (or anyone!) starting a career in tech

(a series of tweets originally sent to @ArrieLay and stored here for posterity…)

Fake it til you make it. Always act like you know what you’re doing, cos You DO – You’re being imperfect, just like everyone else.

Pay attention to people. Focus on empathy. Learn to pair. Learn to collaborate. Celebrate and enable your fellow team members.

Always come to work as yourself. Don’t be afraid to show vulnerabilities, and give others space to show theirs too.

Take risks. Relish your uncomfort zone.

Remember that EVERYBODY feels insecure about their knowledge levels. It’s impossible to know everything, and everybody thinks they are disadvantaged because others know more than them.

Learn to embrace your knowledge gaps. See them as exciting opportunities to learn more. Never be ashamed of them.

Have a questioning attitude, be open about your excitement about learning more. People respond well to it and will help you learn.

Question everything.

Love people. Even the annoying ones. People are great. People are useful. People will help you, whether they mean to or not. 🙂

And for the older amongst us… Age is an advantage, not a curse. Find the wisdom you forgot you had. Age is money in the bank.

Here are some links to some helpful resources for women arriving at or returning to careers in tech:

Resources for Women Arriving at or Returning to IT

Just a bunch of links really, but hopefully useful…

Women returners:

Mums in technology: 

Tech Mums: 

Equate Scotland: 

13 places where women can learn to code:

Code First Girls:

Code First Girls professional courses:

Offline resources:


How to Establish a Culture of Learning

I’ve pulled this out of a larger discussion. The detail of that discussion is not important, but a thing happened there which often happens in many discussions in IT: The possibility was raised that some people might be less well informed than they ought to be.

It’s a bugbear of mine. I think it’s a hidden menace in the software industry so I wanted to put something here.

And no, I do NOT think the menace is that people in IT are ill-informed. Quite the opposite: I think the menace is that we constantly judge one another for being (we believe) ill-informed.

I am often reluctant to admit that I don’t recognise a piece of IT terminology, because I think I might be judged for that. I think it might lead to people taking me less seriously in the context of whatever the discussion is.

But here’s the thing: There are thousands of books out there. Thousands of principles and phrases which encapsulate software development principles. Every single one of us will have books that we have not read and yet which some of our fellow colleagues believe are crucial to a good understanding of software development. Every single one of us will have phrases and terminology we have, by whatever chance, not come across before, even though they may seem crucial to a particular colleague.

There is a temptation sometimes to judge one another for not having knowledge of a particular thing. This attitude feeds strongly into impostor syndrome and the insecurities we all feel about how well equipped we are to do our jobs. The solutions to this are:

a) To rejoice whenever we encounter somebody who doesn’t have knowledge of one of our favourite things. It means we get to introduce it to somebody new! Hurrah!

b) To not assume that just because somebody has not come across a particular way of framing a problem or solution, does not mean they do not have knowledge and understanding of the underlying principles.

c) To always always feel safe to admit any gaps we might have, as that encourages others to do the same. This means we all get to learn more and foster a culture of continual learning and experimentation, which is crucial to good software development.

d) To have faith in the fact that we are part of a larger community of seasoned experts. This compensates for the fact that there is NO SUCH THING as a fully informed individual. We all have gaps. But together we can inform one another and create a powerful force.

Machine-Learnt Algorithms and Unconscious Bias

I attended a web foundation lunch a couple of weeks ago, around many issues related to data and ethics. I have to confess a lot of it went over my head. Since then I attended a talk by Cathy O’Neill which made some things a lot clearer to me. I’ve bought her book Weapons of Math Destruction, so hopefully I’ll have some more cogent notes after I’ve read that.

But in the meantime, here’s one small thought I had about algorithms and patterns:

As human beings we’re very good at identifying patterns, without thinking about it. So, if my experience is that the vast majority of women I see in technical meetings are admin staff, then whenever I encounter a woman in that context, I will guess that she is non-technical.

In a similar way, the machine-learnt algorithms are making assumptions and developing decision-making processes based on patterns identified in existing data. As human beings, we can train ourselves and each other not to make assumptions based on previous experience, and to be open to new possibilities. Is this also something which is / should be / can be built into machine-learnt algorithms?

Unconscious Bias vs Cognitive Bias


What is the connection between unconscious bias and cognitive bias?


Unconscious bias is a term used to describe people making assumptions about sub-groups within society. They are not automatically conscious of those assumptions, or the impact they have on their own behaviour and reasoning.

So, for instance: Society – and my experience – has encouraged me to believe that women are not likely to hold senior technical roles. Therefore if I walk into a meeting of senior technologists and there is one woman present, I might assume she is there in some other capacity – eg some kind of administrative role. If you ask me whether I believe women are less capable than men of being senior technologists, I will say no, but my unconscious bias still leads me to draw unhelpful conclusions, and may cause me to treat this woman in a way I would not deliberately choose.

Cognitive bias is the phenomenon where people used flawed judgement to assess data. Those flaws are caused by the brain’s tendency to be biased according to various factors.

So, for instance, I may listen to two political arguments and assign greater validity to one than the other. I will believe that my assessment is based on rigorous logic whereas in fact, I am simply drawn towards the argument that confirms beliefs I already hold (this is called “confirmation bias”).

Another example is the gambler’s fallacy: If someone tosses a coin ten times and it comes down Heads every time, they might believe that it is more likely to land Tails on the next toss. In fact the likelihood is still 50:50, just as it always was.

Cognitive biases are a very heavily theorised & researched concept within psychology, whereas “unconscious bias” is just a description rather than a clearly defined technical term.

The two terms are related, because cognitive biases may well inform some of our unconscious biases, but they are not generally used in quite the same context.

Cognitive bias:
Unconscious bias: