What do you get when you throw four talented data scientists in a room to serve on a panel about female inclusion in the data industry? We and Big Data Beard had the pleasure of finding out on February 22, 2019 at DSS Austin when we co-hosted a diversity and inclusion inspired panel. For one riveting hour, Randi R. Ludwig, Data Scientist at Dell, asked Eunice Chengdou, Founder and CEO of DataGig, Mo Johnson, Director of Ethics at Data for Democracy and Hillary Green Lerman, Product Manager at DataCamp profound questions to spark a meaningful discussion about diversity.
ON PARTING THE BIG SEA
When asked to distinguish between tech events that target women exclusively and large conferences that span the whole tech industry, panelists had varying opinions. Chengdou finds that the energy at women-only conferences is significantly more inspirational. “I’ve been to a lot of large tech conferences, but every time I’ve gone to a women-only conference, there’s a good emphasis on female empowerment because most women want to help other women,” she said. “There’s a huge difference between going to a conference where I’m a minority and a conference where I’m being celebrated.” Johnson on the other hand, feels that her field already incorporates the respect for diversity that she requires. “When you bring ethics in to the picture, most people already have the capacity build around discussing gender, race and socioeconomic inequalities in relation to tech,” she explained. “In those spaces, women’s voices are represented by design.”
ON MANAGING TOKENIZATION
When asked to compare the inherent skill-sets of men and women, Lerman had some crucial insights. “I reject the question, ‘What do women bring?’ because we would never ask ‘What do men bring?’. We assume, that men inherently have value and we should assume the same about women,” she said. Furthermore Lerman believes that applying the gender divide to skillsets ultimately inhibits everyone involved. “Saying that women have certain inherent skills really deprives both women and men of their agency in developing new skills,” she said. “It’s expected that women take on all the soft, fuzzy work for the whole team without any extra pay.” Johnson added that defining people by their skill-sets has implications apart from gender. “Extraversion and gregariousness doesn’t always associate with analytics skills. The fact that one person can be more than one thing is something society hasn’t figured out,” she said.
Even if tokenization is a product of the recognition of diversity’s importance within the data science industry, the panelists were overall reluctant to accept opportunities that involve their being the “token female” in the room. “If you are only reaching out to me with an opportunity because of the way I look, I don’t feel valuable,” said Chengdou. Nevertheless, searching for heterogeneity within one’s network is undeniably a positive thing if approached correctly. “If you look around you and everyone looks the same as you, it’s your responsibility to try and fix that,” Lerman said.
ON SCHOOLING LITTLE FISH
The concept of mentorship as a way of empowering women in STEM is not new, but all panelists were significantly aware of its impact on their own lives. “I have so many women in the tech space and entrepreneurship space that I look up to and I was bold enough to reach out,” Chengdou said. “Overtime, I realized that people really want to help and that nobody has to do this on their own.” Johnson is also passionate about using mentorship to empower female talent within the data industry. “I try to think of spaces can be created through mentorship and how concepts of mentorship can flourish within communities. We need to think about how we can structure empowerment at every stage of a women’s career development journey,” she said. Ludwig stressed the importance of making sure women are empowered early enough in their lives to make a difference. “I’ve read studies that say that girls have decided not to pursue a career in math or science by the 4th grade. So much of our context is set by society at a young age,” she said. Johnson responded by suggesting Coding Unplugged as a viable gateway into the world of STEM for young girls.
This initial discussion of mentorship took an interesting turn when panelists began to consider the limited female presence in the data space. “As more and more women have the opportunity and availability to go into STEM, we need to keep in mind that there are fewer women at the top of the field than men,” said Johnson. “We need to save some emotional space for the growth of other women in the industry.” Johnson feels passionate about making sure that the responsibility to respond to the influx of female entry into the industry does not negatively impact the careers of the women who have already fought for and achieved their presence. “There’s a huge place to have women mentorship, but it has to be shared so that women are all able to focus on their career rather than taking full responsibility for bringing new talent into the field,” she explained. “We can’t expect a small group of successful women in the field to be solely responsible for the younger generation.” In other words, men need to also take on mentorship roles for the increasing population of women entering data-driven fields.
ON THE SCOPE OF AUTOMATION
Artificial Intelligence was the focus of DSS Austin at large and the panel made sure to incorporate it into their discussion of diversity and inclusion. The panelists agreed that AI has a long way to go before it helps promote diversity. “Anything involving AI and training datasets has had unfortunate blind spots towards women, people of color and everyone not involved in the training set,” said Johnson. She continued with the idea that the push towards diversity has to be adopted by the current leaders in the industry. “We must demand that empowerment is shared within the tech world by people who have been at the table for so long,” she said. “We don’t want the bias of society to get encoded into our programs by AI.”
NOT SO SIMPLY HIRED
The panelists wrapped up their discussion by brainstorming ways that both hiring procedures and work culture can evolve to promote diversity. Lerman pointed out that “hiring language often excludes people in the field who have not been primed to think of themselves as the ‘best and brightest in the field.” She added that “phrasing your hiring promotions that way attracts a certain type of personality of which you may not want your whole organization to be made.” When Lerman criticized the excuse many companies make that only men applied for the position, Ludwig suggested that “companies should try to pool candidates from diverse communities rather than passively accept whatever they get.” Johnson wrapped everything up with a compelling call to action: “Folks talk a lot about hiring the best person for the job, but I want the focus to shift to the idea that there are a group of people who are talented and could do this job and who we select on that group will depend on many things. Some of them will be issues of diversity and inclusion.”