Data grows more intertwined with the everyday and more involved in important decisions. However, data is biased in many ways from collection, to analysis, and the conclusions, which is a problem when it is often intended to provide an objective point of view. In their recently released manuscript for Data Feminism, Catherine D’Ignazio and Lauren Klein discuss the importance of varied points of view:
The double-edged sword of data shows just how important it is to understand how structures of power and privilege operate in the world. The questions we might ask about these structures can relate to issues of gender in the workplace, as in the case of Christine Darden and her wrongly delayed promotion. Or they can relate to issues of broader social inequality, as in the case of predictive policing described just above. So one thing you will notice throughout this book is that not all of our examples are about women–and deliberately so. This is because data feminism is about more than women. It’s is about more than gender. Put simply: Data Feminism is a book about power in data science. Because feminism, ultimately, is about power too. It is about who has power and who doesn’t, about the consequences of those power differentials, and how those power differentials can be challenged and changed.
In the interest of making the published work as complete as possible, D’Ignazio and Klein made the manuscript public and are ready for feedback.