Women in Tech: Top Cities

sunquestion

I just looked at 2016’s Best & Worst States for Women’s Equality and something jumped out at me.

Ohio ranks 40th out of 50. Why does that ranking seem familiar?

In the 2016’s Best Cities for Women in Tech our ranking was similar. I live in Columbus, Ohio.

My city ranked 46th out of the 58 cities that qualified for the ranking.

For contrast, let’s look at the city I grew up in – Minneapolis, Minnesota.

Minnesota ranks 7th out of 50. Minneapolis ranks 13th out of 58.

OK, Columbus, Ohio. It’s time for a pep talk. We can do better. We will do better. We will find a way to do better.

That’s all I got for now. Read the rest of my blog to find out what I think we can do to to get better.  :-)

And leave your comments about what you’d like to do or see done to raise our rankings…

 

Women in Tech: The Pipeline

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The lack of gender balance in tech is not a pipeline problem.

Women leave tech at more than twice the rate men do and research into why they leave points to gender bias in tech.

Many studies show that when bias is removed from the hiring process, more women are hired. 

What about the fact that less women than men have a computer science degree? Well it turns out that having a computer science degree isn’t really a valid prerequisite for most junior dev jobs, or for advancing into higher levels after that role. All you have to do is look at the success of the coding bootcamp industry to learn that. Also, it’s possible that nearly half the men currently working as programmers don’t have a computer science degree.

Just about every coder uses Stack Overflow, a question and answer site for programmers. In the 2015 Stack Overflow Developer Survey it was revealed that 48% of respondents never received a degree in computer science. The alternative pathways those without a degree took to get into tech were: being self-taught, getting on-the-job training, learning through a mentorship program, receiving an industry certification, taking online classes, taking some technical university courses, or going to a coding bootcamp.

Even at Google, the proportion of people without any college education has increased over time. As of 2014 it was as high as 14% on some teams.

If we want to quickly change the gender balance in tech, we can help women who want to get into tech have access to alternative pathways. We can work to retain the women who are in tech and draw the women who have left back in. And we can support hiring managers in designing ways to select for people who truly have the skills and potential, instead of (probably unconsciously) applying a standard (requiring a cs degree) to female applicants that clearly hasn’t been consistently applied to men.

Women in Tech: The Myth of The “Real Programmer”

Screenshot 2016-08-16 14.03.37

 

I was just listening to episode 101 of the Code Newbie Podcast and realized there are some important messages in it that I want to amplify. What the guest of the episode has to say can help us all move closer to gender balance in tech. (It’s also helpful for other aspects of diversity.)  Here’s the episode description…

Jacob Kaplan-Moss is often credited for co-creating Django, one of the most popular web frameworks written in python. But that’s not exactly true. He’s also given credit for being an amazing developer. But that’s not very accurate either. Jacob tells us the true story of Django’s creation, why he calls himself a mediocre programmer, and unpacks the concept of the talent myth.

Jacob talked about 4 myths that have a negative impact on diversity in the tech industry…

1) The Real Programmer
2) It’s not OK to get into tech for the money.
3) You have to have some “special” aptitude in or order to be a good programmer.
4) “Talented Individuals” are the source of good tech

The myth of the “real programmer” reinforces the idea that only a narrow type of person with a narrow approach to creating technology is welcome in tech. Jacob points out that when someone says “that person isn’t a real programmer” what they are actually saying in a coded way is “that person isn’t like me.”

The second myth is locked into the idea that you have to program out of a “pure passion.” One way people screen for that “pure passion” is by looking to your childhood. Were you obsessed with computers from an early age? Did you take one apart just to see how it worked? However, people who are from groups that are underrepresented in tech faced barriers to having that early experience and from feeling like a passion for computers was something they could build an identity around. Programming is for everyone. It’s even for people who want to be in the industry because that’s where good jobs and job security are. It’s even for people who have other interests outside of programming. It’s even for people who want to have life balance.

The third myth is that programming is some exotic skill that only a genius can do well. The truth is that anyone who has average intelligence, and a good level of grit, can learn to code and become a good programmer.

This is not meant to insult programmers by saying what they do is easy. It’s meant to point out that the mind of the average person has more capability than we generally acknowledge. It’s also good to remember that technology is constantly expanding the mental capacity of the average person by supplementing information storage and processing power with external systems and tools.

The fourth myth is that “talented individuals” are the source of good tech. The reality is that good teams, with people who have varied perspectives and strengths, create good tech. The reality is that people who have emotional intelligence and excellent communication skills and know how to contribute to psychological safety for their team members help create the kinds of teams that produce the best solutions to tech problems.

If we continue to promote the myth that there are only two kinds of programmers – the rock stars and the failures, women will be turned away from the industry. Part of this is because gender bias makes people less likely to see women as rock stars or potential rock stars. And part of this is based in the reality that women tend to rate themselves lower than men do. 

To learn more, listen to the Code Newbie episode and watch Jacob’s Pycon Keynote: “Talent Myth”

Women in Tech: Visibility

I was just contacted by a middle school teacher in California who’s looking for photos and bios of women in technology to share with her students. She was mainly after “everyday” women in tech, not the most successful women in tech. She wanted to show her students women in tech they could possibly see themselves in.

The first thing I thought of was the “I look like an engineer” campaign. The campaign was sparked in 2015 when people responded with disbelief/harassment to an image of a female software engineer.

The image was part of an advertisement campaign created by OneLogin, a software company in San Francisco. The firm placed ads up to invite engineers to join their team by having a few of their employees pose for the ads. Isis Anchalee’s ad caught more attention than her coworkers who also participated in the ad campaign. The ad was posted online and her particular ad received comments saying she was not a true engineer. Anchalee took to social media and posted a picture of herself holding a piece of paper describing her job and a caption with the hashtag, #ILookLikeAnEngineer. She wrote a great follow up blog post a year later describing some highlights from the movement that followed the original post.

Michelle Glauser is offering a free poster from the campaign with photos of underrepresented people in engineering. Find her on Twitter and give her an email..

Screenshot 2016-08-16 03.45.36

I also thought of the “Women of Silicon Valley” blog and Facebook Page.  Lea Coligado, a computer science student at Stanford, got the idea to start Women of Silicon Valley after noticing that the public image of the tech world was “invariably male.”

Here’s an example of the kind of posts you’ll see…

Screenshot 2016-08-16 04.28.42

There’s also the beautifully organized and presented “Techies Project.”  The project covers subjects who tend to be underrepresented in the greater tech narrative. This includes (but is not limited to) women, people of color, folks over 50, LGBT, working parents, disabled, etc. The project has two main goals: to show the outside world a more comprehensive picture of people who work in tech, and to bring a bit of attention to folks in the industry whose stories have never been heard, considered or celebrated. If you are looking exclusively for stories of women in technology, the site makes it easy for you to filter the profiles by keywords.

And of course there are frequently articles published like “The Most Powerful Women in Tech” and “Elle’s 2016 Women in Tech” . Those can be useful if you want to show young people images of women who are at high levels of success in the tech industry.

There’s also the untold history of women in tech. Sharing the stories of women who have made an impact on the field of technology from day one can help break down stereotypes. The white house created a great resource for sharing those stories.

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The World Economic Forum has a post called, “5 female coders you have probably never heard of who changed the world.”

There’s also this excellent, “Women’s History of Silicon Valley” article.

One could also show students the trailer to “CODE: Debugging the Gender Gap.” It features many women in tech. And if there’s enough interest, a teacher may be able to host a screening of the film at their school. The target audience is adults, but I can imagine it also engaging and exciting young people.

And if you’re ever designing something related to the tech industry and you want to use photos that break the stereotype, here’s a resource.

Let me know if there are any good women in tech visibility resources I didn’t mention, and I’ll add them to this post.

Upcoming Talks

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On July 19th, a conference organizer from MIT reached out to me and invited me to speak at an upcoming Women in Tech conference at MIT Media Lab. She asked me to give a talk on how men can serve as allies for women in tech. She’d found my work online.

About a week after that request came in, the organizers of the first Women in Tech conference in Columbus reached out to me and asked me to speak in August at their first meetup event for the Columbus Women in Tech group they created. I asked if I could give the talk I plan to give at MIT so that I have a chance to do a run through before that event and they said yes. The talk will be loosely based on a talk I gave in February at Columbus Web Group. However, I’ve learned so much more since then. Therefore, this talk will be significantly different.

Also, back in April, the President of Undergraduate Business Women’s Association and the President of the Association of Computing Machinery Committee on Women in Computing reached out to me. They had the idea to collaborate on an event for both of their organizations. The event will take place in October, it will be open to the public, and I will be speaking on how women can use technology skills to empower themselves and accelerate their careers.

The Schedule:

Monday, August 15th, 2016

Talk Title and Link to Register:

Gender Balance Champion: What Men In Tech Can Do

Time and Location:

6:00 PM

Information Control Corporation (ICC)
2500 Corporate Exchange Drive
Suite 310
Columbus, OH

Talk Synopsis:

Men in tech are uniquely positioned to be champions for structural change in the tech industry. However, to be most effective, they must be well-versed in the benefits of gender balance and familiar with research-based design solutions to gender bias.

Lauren will discuss:

• Why gender balance is a win-win for everyone

• Core concepts that underpin becoming an effective male change agent

• How to skillfully manage unconscious bias and create a truly inclusive environment

• How to gamify your learning process with a tool that Lauren is creating called, “Gender Balance Champion.”

Speaker Bio:

In 2014, Lauren Kinsey became focused on understanding the intersection between technology and gender. When she gave a talk at TEDx Columbus Women in 2015, on How Women Can Hack into Tech, she created a website to accompany it. Every day, women continue to use the site to find alternative pathways into tech. The TEDx talk opened up ongoing conversations with people in the tech industry. Lauren talked with many men in tech who said, “I want to make an impact, but what can men do?” In 2016, Lauren was invited to give another talk and she used it as an opportunity to address the questions men had been asking her. The hunger for the information she shared was obvious from the responses she got locally. In addition, a conference organizer from MIT saw a video of the talk and invited her to come speak at MIT Media Lab’s Women in Tech Conference. Lauren wondered how to best encapsulate and communicate what she had continued to learn since her last talk. In a flash of inspiration, it came to her – GAMIFICATION. She immediately set to work designing  the game, “Gender Balance Champion.”

Agenda:

6:00-6:30- Food and networking

(Free food and beverage provided)

6:30-7:30- Presentation

7:30-7:45 – Q & A

7:45 – ? – Networking

———————————————————————

Friday, September 9th, 2016

MIT Media Lab’s Women in Tech Conference

———————————————————–

Wednesday, October 12th, 2016

How Tech Skills Can Empower Women and Accelerate Their Careers

7:00pm

(More details will be posted here soon.)

Women in Tech: Hiring

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Photos used in this design are from #WOCinTech Chat

I put together this blog post so that when people ask me how to improve the hiring process, I have a quick link to share. If you’re after soundbite answers to a complicated issue, I did my best to help you. However, you will still have a lot of reading and deciding to do. Figuring out how to design our organizations to counteract the negative effects of unconscious bias is in beta stage. You’ll need to implement practices based on the most recent and best thinking on the topic, but then you’ll also have to be willing to adapt as new data comes in. But you work in tech, so iteration is something you’re comfy with. So what are you waiting for? Read this post and then move fast and break something.

I have immense respect for the people behind Project Include, and I highly recommend reading everything they have to say on their site. Another source of information I trust is Iris Bohnet, a behavioral economist at Harvard. Iris has so much to add to the conversation about how to create gender balance in tech because of her behavioral design insights.

These two excellent sources of information don’t always agree. For example, Project Include recommends two-on-one interview panels and Iris Bohnet recommends avoiding panels. Iris Bohnet justifies her suggestion by pointing to how research shows that panel interviews are influenced by groupthink, and therefore one-on-one interviews will give you more objective data points to factor in together in a final process.

If you take the time to read Iris Bohnet’s book and HBR articles and take the time to read the Project Include website and suggested resources for further study, you will be in a strong position to implement valuable changes to your hiring practice.

Project Include-

One of the team members behind Project Include whom I admire the most is Freada Kapor Klein. I’m going to share a bit about her with you so you understand some of the badassery behind the advice you’ll read there.

Freada Kapor Klein, Partner at Kapor Capital and Kapor Center for Social Impact, and Founder/Board Chair at Level Playing Field Institute. For over four decades, Dr. Freada Kapor Klein has worked at the intersection of racial/social justice and tech, with particular expertise in the fields of human capital and bias mitigation. Dr. Kapor Klein is a pioneer in multiple regards: She started the first group on sexual harassment in the U.S.; launched innovative diversity and inclusion initiatives at a rapidly growing tech company long before it was front-page news; and is a seasoned researcher whose studies are utilized by countless organizations around the country to understand what hidden bias looks like and how the cumulative effects of subtle day-to-day experiences drive talent out the door. Over the years, Dr. Kapor Klein has advised thousands of CEOs who are responsible for millions of employees. She has lived through several eras of discussions about diversity followed by disinterest, and has been able to uniquely share what we can all learn from the peaks and valleys of the diversity movement.

Check out the page on the Project Include website about the Employee Life Cycle and read the recommendations for improving tech hiring processes.

What Works: Gender Equality by Design

Author, Iris Bohnet explains that it’s very hard to eliminate our biases. Fortunately, we can design organizations to make it easier for our biased minds to get things right. Techniques Bohnet discusses in her book that have been shown to help with unbiasing the process of attracting candidates and making hiring decisions include –

  • Use gender neutral language in job descriptions. Tools exist to help you do this. One example is Textio. It recognizes tens of thousands of permutations of gendered language, and the patterns change all the time based on what’s happening in the market. You can’t achieve the same results with static phrase checklists.
  • Interview consistently. Ask the same interview questions in the same order. Score each answer immediately after it’s provided. Unstructured interviews are loved by hiring managers, however numerous studies have found them to be among the worst predictors of on-the-job performance. They also leave lots of room for unconscious bias to influence the outcome.
  • Use work-sample tests related to the tasks the job candidate will have to perform. If you think whiteboard interviews accomplish this goal, click on these words.
  • Precommit to the desired candidate qualities and how you will weigh them when selecting candidates. 
  • Compare candidates to each other. When we evaluate job candidates, we tend to want to compare them to candidates we’ve always seen or people who are typically in the job we’re hiring for. This leads to hiring the same types of people over and over.
  • Blind is Best. As much as possible design your interview process so that your interviewers aren’t exposed to non-relevant data about the candidate. Non-relevant data that can trigger bias can include gender, age, race, ethnicity, personal beliefs, socioeconomic status, etc. Slack attributes a blind  skills testing process to much of their success with diversity.

Gapjumpers

Use performance audition challenges to evaluate talent on their work performance rather than keywords on a resume. Avoid discarding desirable talent that does not fit pre-conceived notions of what talent looks like and where it comes from. Diversify your workforce for skills so that gender, education, and background don’t matter.

Applied – 

Applied is a collaboration between the Behavioural Insights Team and Nesta focused on using behavioural science to help organisations do better, fairer recruitment. It does this by using the best evidence to identify and remove unconscious bias from hiring practices.

The Behavioural Insights Team is a social purpose company co-owned by the UK Cabinet Office and Nesta with the aim of applying behavioural science to social problems.

Nesta is the leading UK foundation that supports innovation for social good.

Interviewing.io –

A tool for helping you focus on desired skills rather than other noise.

Unitive –

The software’s aim is to make HR functions like recruitment, hiring, and promotions more efficient and bias-blind.

Blendoor –

A mobile job matching app that hides candidate name and photo to circumvent unconscious bias and facilitate diversity recruiting in tech companies. Studies have shown that two identical resumes with only a name difference (i.e., Joe to Jose) can yield 100% difference in the response rate. Blendoor’s goal is to highlight the information that’s most relevant to a candidate being a “good fit” independent of race, gender, ability, military history or sexual orientation. Blendoor was founded by a Stanford Engineer and MIT Sloan MBA.

Interview Edge- 

They’ve helped hundreds of companies gain a competitive edge in their hiring practices for over 30 years. They provide competency-based behavioral interviewing training for interview teams including hiring managers, recruiters, and interviewers.

Questions:

Should we have women on our interviewing team?

Cisco is ensuring that job candidates encounter at least one interviewer of their same gender or ethnicity, a practice that has resulted in a roughly 50% increase in the odds a woman will be hired for a given position, said Ruba Borno, a Cisco vice president and chief of staff to Chief Executive Chuck Robbins.

The Cisco story is one compelling case study. However, Iris Bohnet points out that it’s entirely possible for men and women to be biased against women for roles that are stereotypically associated with men. Iris Bohnet doesn’t advise to avoid having a diverse interviewing team, she only advises against thinking that by doing that alone you’ve solved the problem. Here’s what Project Include has to say on the topic…

The people who make up the panel of interviewers matter immensely, especially when interviewing a candidate who is part of an underrepresented group. Though it’s tempting to have the one woman or person of color on your team interview every candidate who may be able to boost the company’s diversity stats, that shortcut that doesn’t necessarily get the desired results. Not only do interviewers develop fatigue, but if they lack interview skills, the candidate will have a bad experience. What matters most is that every interviewer is respectful, professional, and conscientious throughout the stressful interview process.

Any other questions? Go ahead and leave them in the comments or send them to me through the contact form at womenintechsuccess.com or genderbalancechampion.com. I’ll do my best to add the answers to this post.

Defining “Women in Tech”

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Today I was having lunch with a leader in the local tech community. She and I were talking about the percentage of women in tech. She said that she’d heard women were only 10% of the tech workforce. I said that there was a range depending on how the term was defined and the numbers were sliced, and the range I’d heard the most was between 10% and 20%.

This got me thinking about how I’m defining the phrase “women in tech.” It’s important to have a good definition because if it’s too broad the numbers of women in tech could be artificially fluffed up by roles where there’s less gender bias against women and usually less status, impact, opportunity and compensation.

I’m currently doing research for a book to try to answer the question, “What Can Cities Do to Earn a Reputation for Being a Great City for Women in Tech?” I’m interested in the ranking from Smartasset of the top cities for women in tech. I think the ranking is important because it’s the only one I know of and rankings are useful for giving us a benchmark to work with.

The Smartasset ranking is based on data from the last US census. The data used comes from the following category…

Computer and Mathematical Occupations

This major group comprises the following occupations: Computer and Information Research Scientists ; Computer Systems Analysts ; Information Security Analysts ; Computer Programmers ; Software Developers, Applications ; Software Developers, Systems Software ; Web Developers ; Database Administrators ; Network and Computer Systems Administrators ; Computer Network Architects ; Computer User Support Specialists ; Computer Network Support Specialists ; Computer Occupations, All Other ; Actuaries ; Mathematicians ; Operations Research Analysts ; Statisticians ; Mathematical Technicians ; Mathematical Science Occupations, All Other

Computer Occupations, All Other breaks down to…

All computer occupations not listed separately. Excludes “Computer and Information Systems Managers” (11-3021), “Computer Hardware Engineers” (17-2061), “Electrical and Electronics Engineers” (17-2070), “Computer Science Teachers, Postsecondary” (25-1021), “Multimedia Artists and Animators” (27-1014), “Graphic Designers” (27-1024), “Computer Operators” (43-9011), and “Computer, Automated Teller, and Office Machine Repairs” (49-2011).

I don’t know how to figure out what is included in “all computer occupations not listed separately.” And I don’t know why certain computer related jobs are excluded from the “Computer and Mathematical Occupations” category.

Until I figure that out, what I’ll consider when I’m considering whether someone is a woman in tech is if they have one of the following titles…

 

How do you define, “women in tech” and why do you use the definition you use?

The Anita Borg Institute uses this definition

Increasing the representation of women in all technical roles is critical to a company’s innovation. Reflecting ABI’s constituency, we have elected to define the technical workforce as consisting of all technical occupations in computing and information technology. The technical workforce is defined by position, not department. This includes both technical individual contributors as well as technical managers. The definition of technical employees covers four categories: engineering, research and development, and technical design; IT engineering and support; technical services, technical sales and technical marketing; and technical management and leadership. The definition is based on a combination of ABI’s previous research, ongoing review with industry leaders, and a detailed review of standardized lists of positions pertaining to technical roles. ABI’s definition of the technical workforce does not include non-technical roles (such as marketing or finance) nor does it include technical roles that are not related to computing (such as biotechnicians or aerospace engineers).