We do not often question the data and technology we see and use even in our daily lives. Many minority groups are not counted or are misrepresented in our data. D’Ignazio and Klein interpreted Callaghan’s statement, “What we choose to measure is a statement of what we value in health” (page 23) as a measure of who matters in health data collection. In saying this, they are trying to pose that the majority groups with a higher political and financial status are the ones collecting the data and are failing to include minority groups because they do not care about them or think of them.

At the beginning of Chapter one, we read about Serena William’s post on Instagram which ignited a conversation about black women and birth complications that could cost them their lives. To D’Ignazio and Klein’s point, Serena Williams says, “If I wasn’t who I am, it could have been me” (page 23). By saying this, she is trying to highlight the fact that so many black women have not received the special care they require due to their genetics when giving birth and as a result they have had the most complications that ultimately led to their death. She shines a light on the disparity in data collection when it comes to black women specifically suffering from birthing complications. Instead, data is collected generally about women who have birthing complications and not accounting for the greater percentage being black women.

Later in the Chapter, we are introduced to an MIT graduate named Joy Buolamwini who is a black woman that worked on a class project about facial analysis software. In this project she demonstrated a flaw in the facial recognition software when it failed to recognize or “see” a dark woman’s face. After a study, she and a computer scientist found that “dark skinned women were up to fourty-four times more likely to be misclassified than lighter-skinned males” (page 30). This proved that intelligence technology is also very much flawed and fails to represent minority groups as well.