Large Silicon Valley technology companies such as Google, Facebook, Apple and Oracle fuel the global economy and have a strong hold over our everyday lives. The products they build are used by everyone, and yet most technology companies themselves do poorly on hiring and retaining minority workers. A lot has been written about diversity and representation in technology giants, but surprisingly, there’s very little data around the issue.
Reveal has been collecting diversity data and deeply reporting about this issue for almost a year. Some companies gave us data when we asked. Most refused. So Reveal filed Freedom of Information Act requests with the Department of Labor and collaborated with an academic who has special access to the data. We have even sued the Department of Labor for this data. We’ve seen significant response from technology companies. But there’s more work to be done.
Only a few companies give out their diversity statistics and even fewer give any data on how they are retaining their minority employees. The upper echelons of these companies are overwhelmingly white and male. Most of the venture capitalists are also overwhelmingly white and male, and experts surmise that they fund people who look like them, who in turn hire people from their networks. But again, there’s very little publicly available and reliable data on who and how venture capitalists fund.
Through participating in the Civic Data Solidarity project, Reveal is interested in understanding how to place diversity in Silicon Valley into a larger context. Being involved in the Civic Data Solidarity Project has helped us think about the issue more holistically and about lateral connections of this issue with other issues. We’re interested in connecting the dots between diversity data and other datasets. For example, we’d like to study connections between diversity and representation in technology and representation in the venture capitalism world. We want to know how bias creeps into hiring practices and if hiring using artificial intelligence reduces bias. We’d like to understand the compositions of race and gender in ivy league universities and study relations between compositions in tech companies.
By studying and understanding these connections and relationships, we would want to contribute to developing a protocol that can be used to easily connect different datasets. We’re also interested in raising awareness about this issue by being a part of this public dialogue with different stakeholders as a part of the Public Knowledge initiative.