I have never questioned any data done by the analysts before because I believed they were accurate and based on solid-proof data. It is true we ignore the data and science that are used in our daily lives. One of the biggest example of this statement is Serena William’s pregnancy. If she hadn’t posted about the complications she experienced during her pregnancy, then many women and most specifically black women wouldn’t have shared their experiences as well. Serena Williams took her to social media; “Black women are over 3 times more likely than white women to die from pregnancy or childbirth related causes.” She mentioned if she was not a tennis player, she would have experienced the same disparities the other Black Women experienced.

For example, to prove how data science prioritizes different people and show bias included in data science, Kimberly Seals and her son started an app that worked on the issue of how minority groups are treated differently from white women. “One of the major contributing factors to poor birth outcomes, as well as maternal and infant mortality, is biased care.” Serena Williams was given all the care and treatment she deserved because she was an international star. Being inspired from Serena Williams story, Seals was motivated to become the voice of women who were not given the same treatment, and suffered bias, which led to mortality.

Another example from this chapter was when the data analyst of Target created an algorithm that figured out pregnant women by analyzing the purchases they made and started sending them pregnancy coupons to boost their customers. But this turned out to be a negative impact on a teenage girl who was pregnant and didn’t inform her family. “For Target, the primary motivation was maximizing profit, and quarterly financial reports to the board are the measurement of success. Whose goals are not prioritized? The teenager’s and those of every other pregnant woman out there.” I believe all the big corporations target the minority groups because they know they can earn money from them.

Another example is when Amazon was looking for new employees and used their prior algorithm to search for new employees. But, this didn’t work because the model was trained to find male instead of women. Once again women who were qualified were ignored. People in dominant group are always favored even though they are not aware of it. To change this, we need to take steps by actually providing accurate data and changing the models and start working on datasets that have not been collected yet because of bias and lack of social and political will.