Combining computational and humanistic approaches to cultural data with a focus on gender and race.
My book Data Feminism, co-authored with Catherine D'Ignazio, outlines seven principles for more just and equitable data science informed by intersectional feminism. My own quantitative work involves using NLP/ML to analyze large cultural datasets, particularly concerning issues of race and gender. I direct the Digital Humanities Lab at Emory University, which seeks to involve students of all genders in field-advancing work.
The principles of data feminism offer a set of guidelines for those currently working with data, those who want to work with data, or those who want to refuse to work with data. My own work and the work of the Digital Humanities Lab models how these principles can be put into action, illustrating how quantitative methods can be used to expose how power currently operates in our datasets and data systems and how it can be challenged and changed.