Uncertain Archives: Critical Keywords for Big Data

Big Data, Data Science, Feminism

Scholars from a range of disciplines interrogate terms relevant to critical studies of big data, from abuse and aggregate to visualization and vulnerability.

Co-edited with Nanna Bonde Thylstrup, Daniela Agostinho, Annie Ring, and Kristin Veel

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.”

Disentangled No. 1 (2017) by Nora Al-Badri and Jan Nikolai Nelles, from the chapter “Technoheritage.”

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.


  • LSE Review of Books, Catriona Gray, May 2021.
  • Nordic Journal of Library and Information Studies, Herbjørn Andresen, June 2021.


The MIT Press, Cambridge, MA


Independent Research Fund Denmark, Carlsberg Foundation