Author: Ugonma Nwankwo, Michael Pisa
Publisher: Center for Global Development
Publication Year: 2021
Summary: The following article discusses how evidence-based policy making is ineffective when it relies on biased information, a potential source for this is bias in the datasets. In the U.S., women make up 18% of data scientist jobs and in lower-income countries, that stat is even worse. In the data value chain, which includes collection, publication, uptake and impact, there’s a chance for bias. Data scientists, whether they can help it or not, imbed their values in the data they handle. Without women, policies will not be designed and implemented in ways that will not harm them. Some examples of this are crash dummies, hiring algorthims, and safe public restrooms. The first way to address this problem, is to address the lack of women in STEM related subjects in higher education. There are several initiatives to increase women in this field. One of the most important efforts is to keep people in the decision making process, who are affected by the governance decisions.
