Why and how

Premise: Our brains learn by recognizing patterns. We see patterns everywhere and we don’t notice that we are learning them. Advertisers show pictures of products next to pleasing images; we form associations.

Premise: We notice gender immediately. We can’t even think about a person without first deciding “he” or “she”.

Premise: Most serious programmers are men. If you question this, you don’t work in the US.

Consequence:
Without intending to, we associate programming with men because our brain has silently observed this pattern. When we see a woman at a user group the brain signals, “recruiter.” When we see two resumes, the one with the masculine first name signals “serious programmer.” This isn’t anyone’s fault. Our brains just work this way. The really senior, wise programmers are the graybeards. The term reveals and reinforces the pattern.

On our side, women don’t see the technical side of computer science as a career path. I see very few role models at the top – none yet in the US in the JVM space. I see many groups that are all male. The first and hardest step in getting more women into the influential groups is to get one. It never occurred to me to aim for the ranks of technical leadership, partly because every speaker on the NFJS tour is male. Those were the only conferences I had attended. While I love public speaking, I didn’t picture myself in front of a group of developers – until someone asked me. Until someone offered me the opportunity, and then I jumped at it, and excelled. There are hundreds or thousands of other women in IT who are quietly doing their jobs, who never thought seriously about leading as a technologist, who would flourish if approached with opportunity.

Steps to counteract each of these causal factors:
Recognise that it happens and try to compensate consciously. The least biased people are the ones who recognize their bias. When possible, look at resumes without a name at the top. When orchestras started blind auditions, they magically went from male-dominated to evenly divided.

When you’re doing those blind selections, it helps to know that ‘they’ is grammatically correct in the singular. Try to use it as a gender-neutral pronoun, and realize how deeply embedded gender is in the American psyche.

Change the patterns we (and future people) see by getting more women into programming, and more women in prominent positions in programming. Long term, the bias will go away when the pattern goes away. Bring women to the forefront of the field. If you’re selecting a leader and the best woman you can find is not as qualified as the best man you can find, (1) check your numbers to make sure unintentional bias isn’t working against her, and (2) hire her anyway. She is smart and she will rise to the occasion. She is not as experienced because women haven’t been given these opportunities in the past. So give it to her. Next round, she will be the most qualified.

Am I advocating affirmative action in hiring? No, I’m advocating blind hiring as much as is feasible. This has worked for conferences that do blind session selection and seek out submissions from women. However, I am advocating deliberate bias in favor of a woman in promotions, committee selection, writing and speaking solicitation, all technical leadership positions. The small biases have multiplied until there are almost no women in the highest technical levels of the field. This imbalance is both most egregious and most harmful. It impacts impressions of women considering entering the field, of women in the field picturing their future, of teachers, of hiring managers, and of current leaders selecting and encouraging peers. Participation in leadership is determined by fewer individuals, and if these individuals take deliberate action to bring women in, the impact these women have and the impressions they create will take root in brains throughout our industry.

This article is a great breakdown of the bias that exists: http://www.cnn.com/2012/10/01/opinion/urry-women-science/index.html?c=&page=3