Data scientists leading the charge for gender equity and bias awareness

Welcome to 84.51° Data University, a series of quarterly insights for prospective and current data-science professionals. 

With the start of Women's Month and in celebration of International Women's Day, I am reminded of both the pride and responsibility I carry as a female leader in tech. As a data science organization, it takes activation not only in the way we deliver work, but also in how we manage our organization to ensure that we are modeling values around gender diversity in tech.     Some say it's an art, but it can also be a science. As part of our Data University series, I've interviewed several individuals in different roles and groups in our business that use data science to showcase the importance of advocating for gender equity and bias awareness more broadly.  

Mitigating bias in our work

• Algorithm fairness – Grant Gilkerson, Justin Conley, Joe Bahr and Katie Mendrala 

• Grassroots education on gender bias in emerging science – Johnnalee Kutzke 

• Gender bias in self-report skill ratings – Michael Carrico, Joe Bahr, Katherine Keenan 

Passionate People-Led Teams

• The Insightful Inclusion Council – Samara Caplan 

• Men for Equality – Riggs Burnette 

• Women in Tech – Preeya Mody and Emma Krummenacher 

 Not only am I proud to be a female leader in tech myself, but I am also honored to work for an organization that advocates for gender equity and awareness. Narrowing this list was no small feat - we have a highly active Women in Tech at 84.51° group, a subset of our broader Women's Edge group. In organizations that develop and deliver data science solutions, gender and general bias mitigation cannot solely live within the delivery of the work itself. Addressing bias in both what you deliver and how you manage your organization sends a strong message about your organization’s values and commitment to the cause.  

As you consider Women's Month and your own passion and skills in data science, what ways are you an advocate for gender diversity or bias mitigation? How can you and your organization use data science as a superpower to elevate the underserved and shed light on how we can all do better?