Uncertain Archives: Critical Keywords for Big Data

Big Data, Data Science, Feminism

One challenge of integrating feminist theory with technical disciplines and practices is that feminist scholarship has traditionally been located in the humanities and social sciences. Thus, there is a strong need for building bridges and dialogues between the humanities, social sciences and computer science.

With four co-editors, I set out to do this bridging work in a co-edited volume published by MIT Press called Uncertain Archives: Critical Keywords for Big Data. Organized as alphabetical entries in a glossary, more than 60 contributors interrogate key terms that relate to data, including “aggregate,” “outlier,” “prediction,” and “visualization.”

The goal of the book is to define a critical approach to Big Data–i.e. integrating an analysis of power and inequality into the theory and practice of computation. This book represents a contribution to the emerging field of Critical Data Studies, an interdisciplinary area that brings together scholars from humanities, social sciences, computer science and information sciences around the shared interrogation of the perils and possibilities for data and computation.

Contributing scholars include N. Katherine Hayles, Wendy Hui Kyong Chun, Johanna Drucker, Lisa Gitelman, Safiya Noble, Sarah T. Roberts and Nicole Starosielski.