This is the first year that GHC has a dedicated Organizational Transformation track. If you’re like me, a used-to-be-new-still-kind-of-new industry professional, these talks may sound intriguing. Now that I’ve had my feet planted in my role, I’ve been looking for ways to bring the GHC back to my workplace.
This is where Christianne Corbett’s talk “Solving the Equation” comes in.
Why does it matter if women are working computing jobs?
Christianne lays out how quickly innovation is moving our society, and 50% of the user base is, after all, women. If we’re missing women, we’re missing the target. Short and sweet, and we really don’t need to linger anymore than that.
We took a look at the current status of women in STEM occupations. Women make up 26% of the computing workforce, and when broken down by ethnic background, the numbers are even more abysmal. About two-thirds of women in computing are of Asian decent. For most attendees at GHC15, this news probably doesn’t come as a shocker.
Christianne transitioned into explaining how stereotypes and biases infringe on this so-called meritocracy at tech companies. Has anyone heard about an implicit association test? When scientists analyze word associations, people are much more likely to group “male” and “science” together, and women are just as likely to have these biases.
The data shows that the gender of the presenter impacts perceived competency. Unfortunately, I can definitely say that I’ve experienced this myself. The easier example is based on my ethnic background rather than gender because I’m half-Japanese and can be a bit of an ethnic chameleon with my Western European last name. Multiple people have asked me in my professional life what it is like being a white girl and a developer. My snippy reply: “I’m actually half-Japanese. Does that make more sense to you now?”
This is bad in hiring situations. As Christianne points out, interviewers are often put in a position to believe (or not believe) a candidate’s stories based on a brief meeting. When we have biases that we’re not great at overcoming, how biased are the outcomes of interviews? Christianne pulled up some data and it looked like about 69% of the time, the higher performing candidate is correctly chosen. That’s not bad, but it’s also not good.
Some interviewing recommendations:
– Use objective criteria whenever possible.
(Can you code the code? Instead of: Can you talk the talk?)
– Remove information about an individual that give away gender, age, etc.
(I actually have a co-worker whose made a million Grease Monkey scripts for this on our company recruiting tool.)
– Gender-inclusive policies.
(I always write my candidate feedback using “they” against my learned grammar habits.)
– Increase the presence of women at all levels.
(This one is hard. We’re working on it.)
Also, females that are portrayed as super-stars don’t actually have that big of an impact on women in the field. Women need to be relatable in order to have an impact on other women. I whole heartedly agree. I really believe that women put on pedestals are helpful, but not all that helpful. I need someone fallible to relate to, and a similar background is even better. The longer I’ve follow this computer science track, the hard it had been for me to find these women. (Maybe I should get on that while I’m at GHC15.)
Another shocking data point is that women engaging in conversations about their work report being less engaged, while men report being more engaged. However, when women socialize at work they report being more engaged, and men, less. Christianne explained that there isn’t enough research to draw conclusions, but the hypothesis is that women report being more engaged when they feel like they belong.
I’ve also heard how even non-gender stereotypes in computer science can have an impact on women entering the field. Computer science is perceived as having a low impact on people, with solitary work environments. As it turns out, women are more likely to place an emphasis on having an impact on people. Incorporating communal value into computer science jobs will likely bring more women into the technical workforce.
Harvey Mudd is a text book example of small changes can have a dramatic impact, the college:
– Revised introductory course and split into levels by experience.
– Provided early research opportunities, real world stuff.
– Took students to Grace Hopper–all all female students, even those who have only been in college a few months.
– About 25% of women not intending to major in computer science ended up majoring in computer science.
– Frame adversity as a common experience. (Let them know the major is hard, they’ll be more likely to stick around.)
The college case study makes me wonder what we can be doing in industry to keep women in the field and attract more women to the field. As Christianne points out, after 30 years, women are 50% less likely to be in the computer science field still. The difference between women in the field and those who have left actually just boiled down to workplace environment than personal goals.
What can employers do?
– Create welcoming environments. (No man caves.)
– Provide training and development. (More training!)
– Hold managers accountable. (Yes, yes, yes.)
– Emphasize social contribution. (+1 we want to help people)
– Root out uncivil behaviors. (Leadership can and should stamp out these behaviors.)
– Be proactive and vocal. (Don’t make your entry level employee try to bring this up.)
What can men do?
– Be an ally. Keep the surroundings gender neutral and use gender neutral language. Talk about women’s accomplishments because women often receive backlash.
(Yes. I have really appreciated how my new management goes out of their way to talk about me.)
– Serve as a role model.
(I try, I try.)
What can women do? (Hey! Don’t we do enough?)
– Survival skills. (Cue Destiny’s Child)
– Present your ideas from the perspective of being in the group’s best interest–this takes a lot of effort and puts the burden of the effort on women. Not the best solution, but it can help.
– Seek a support network.
– Focus on building skills to handle stereotype threats, etc.
– Prioritize working on products that have social relevance if it matters to you.
– Seek out opportunities to serve as a role model for young women.
Gender biases impact everyone. Talk about adversity so people know they’re not alone!
And the good news? People are listening…aka money is being thrown at the problem. Be glad that people are work on it.
And a closing slide linking you AAUW to learn more.