Partner Spotlight
These Boot Camp Grads Built an App That Combats Gender Bias—One Word at a Time
Written by Stephen Eichinger on Aug 4, 2020
Related content: University
Editor's Note: After winning, the team behind Wonder Women Editor changed their name to UInclude to pursue what they believe is an even bigger opportunity: a platform dedicated to increasing the representation of women and people of color at work.
When most people think of gendered language, a few common culprits come to mind: manpower, you guys, mankind. But in reality, many less obvious phrases also convey implicit biases. Often featured in job descriptions, male-gendered words like “strategic” and “competitive” can intimidate female candidates—and even dissuade them from applying to open roles.
The winners of this year’s Next Level Contest, USC Viterbi Data Analytics Boot Camp graduates Toshe Ayo-Ariyo, Sonal Patel, and Danielle Ho, are intent on changing that.
Hosted by Trilogy Education, a 2U, Inc. brand, the Next Level Contest is an online pitch competition that offers boot camp graduates from universities across the nation the opportunity to present creative, tech-enabled solutions to real-world problems—“next level” ideas from fields like coding, data, and UX/UI. As part of the prize package, winners receive mentorship from tech and venture capital professionals, free enrollment in a short course of their choice through GetSmarter, and continued support from the Trilogy community.
This year’s event was overseen by a panel of judges from tech companies including Google and Microsoft. Three projects competed for the title: an advertising platform from a team of Columbia Engineering Coding Boot Camp graduates, an education management tool from Georgia Tech UX/UI Boot Camp graduates, and a content editing app—Wonder Women Editor—from Toshe, Sonal, and Danielle.
The winning project, Wonder Women Editor, is a data-driven tool that identifies gender-biased language in job descriptions—and helps employers create a more equitable recruiting and hiring process. Users simply upload content to the platform, then Wonder Women Editor uses machine learning to pinpoint gendered language and supply neutral alternatives.
We sat down with the winning “Wonder Women” team to learn how they’re using their data analytics skills in the fight for workplace equity and women’s empowerment—one word at a time.
Why is Wonder Women Editor important to society?
Danielle: There’s a problem—one we didn’t even know about before reading a research paper in the boot camp. The wording of job descriptions is having a negative impact on women's desire to apply for roles. Hiring managers may not even be aware of this. Using our tool can help them remove gendered language and attract more talent to their companies.
Sonal: Research has shown that companies with diverse workforces perform better and generate more innovative outcomes. Now more than ever, many companies are looking for ways to increase underrepresented employees in their workforce. Our tool is the solution to a problem society is currently experiencing. Job descriptions that use gendered language put people off, making women feel like they don't belong in that environment. We look forward to solving that problem.
Toshe: There are so many frequently used words that we don't even know are gendered. “Strategy” has masculine connotations. “Responsible” has feminine connotations. We use these words frequently without knowing what they subconsciously communicate to people and how they pose a huge barrier for women. Our tool identifies gendered language, alerts users, and gives them suggestions for gender-neutral word replacements. It was very important for Wonder Women Editor to be backed by data, so we modeled our program around a research study. Otherwise, how do we validate that there’s actually a need for this tool?
Let’s back up for a moment. What were you doing before enrolling in the USC Viterbi Data Analytics Boot Camp?
Sonal: I was a mother and homemaker. By choice, I wanted to be with my daughter when she was small, learning to talk and walk. I wanted to be a part of all those precious moments. Now, at six years old, she’s ready to go into the world—and so am I. The data analytics boot camp was very helpful in showing me how to get started.
Danielle: I was working in digital marketing. I’d heard people talking about data analytics, and I wanted to give it a try. I was never a tech person, so I never thought I’d have anything to do with coding. The boot camp was life-changing. I’m a tech person now!
Toshe: I was at Disney working in an operations, finance, and strategy role—not tech-related at all. I found myself doing a lot of data strategy and analytics work on the job, and really enjoyed it. I decided I wanted to learn more about those areas, so I did a Google search and this boot camp was the first one that came up.
How did you come up with the idea for Wonder Women Editor?
Toshe: Since it was a data analytics boot camp, our project was based on qualitative and quantitative analysis. We originally asked the question: Does an increase in female workplace participation increase GDP? Through our research, we found out that it does. One way to increase female workplace participation in the U.S. is to eliminate gender bias in job descriptions, so we built Wonder Women Editor.
Danielle: Then, for the contest, we expanded on the idea and created a tool to address the problem.
How did you divide work responsibilities?
Toshe: We all had our hands in every aspect of the project. But each of us has very different strengths, so we led different processes. Sonal and Danielle led the development of the application, I led data strategy and qualitative analysis, and we all did full-stack web development.
Sonal: We worked collaboratively and followed an agile methodology, dividing the tasks and multiplying the success. Inspirational teamwork helped us to achieve our goal. We were in constant communication with each other.
What was the contest experience like?
Sonal: The competition was very intense, so we felt honored to be a part of it. All the participants presented exceptional and innovative content, and it was a really good learning process for us. When the judges announced the winners, we felt so thankful that our hard work had been recognized. We also appreciate the feedback from the judges. It will help us to take our product to the next level.
Toshe: We were thrilled with the outcome since the competition was pretty strong. One team had already deployed their product, so they were able to answer all questions about successfully going to market. On the other hand, we had just created our tool and are now considering how we’ll take it to market. We were a little nervous because of that and we’re really honored that we won despite not having our product in the hands of users already.
Danielle: It was super exciting to present to the judges, too. I kept thinking: “We put the code together piece by piece. Now the whole product is here, and they’re looking at it.”
How did the boot camp prepare you for building this tool?
Toshe: The boot camp exposed us to most of the technology we used in creating the tool, but it also taught us to be comfortable not knowing everything. Lots of the work we did on Wonder Women Editor expanded beyond knowledge taught in the boot camp. The boot camp made us a lot more comfortable with trying new technologies—not always knowing how to use them upfront, but knowing where to find the right resources.
Sonal: Our product incorporates everything we have learned about and were introduced to in boot camp. We used programming languages like Python, Selenium, JavaScript, HTML, and CSS and did visualization using Plotly, Leaflet, and Tableau. We also used back-end databases like SQL and MongoDB to infuse a machine learning model into our product. The boot camp introduced us to all these technologies and helped us understand how to implement them in real-time.
What’s next for your team?
Danielle: Through the contest, we learned that our product has a lot of potential. Beyond job descriptions, we can target biased language in advertising, resume-writing, and marketing material. Those are big industries that can also benefit from our anti-bias tools.
Sonal: We’re looking forward to taking our tool to the next level while also creating a platform to promote diversity in the workforce. Our platform, UInclude, will provide services for companies that are looking to create a diverse and inclusive workplace and resources for individuals from underrepresented groups. Currently, we are looking to build an advisory board, expand our team, and apply for Y Combinator in a few months.
Toshe: UInclude will address biases against several marginalized groups. Racism, ageism, ableism, sexism—we want to combat all of them. By identifying those biases in language, our tools and platform will give users opportunities to employ more inclusive language in their recruiting material and will provide them with data-driven insights on strategies to increase the representation of diverse talent in their organizations. Our platform will also be a network for individuals from marginalized groups to access career opportunities within industries across the board, but particularly in fields that they have low representation in. One of our main goals and metrics for success is to double the number of women, people of color, and people with disabilities in tech and finance by 2025.
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