This groundbreaking work offers an interdisciplinary perspective on big data and the archives they accrue, interrogating key terms. Scholars from a range of disciplines analyze concepts relevant to critical studies of big data, arranged glossary style—from abuse and aggregate to visualization and vulnerability. They not only challenge conventional usage of such familiar terms as prediction and objectivity but also introduce such unfamiliar ones as overfitting and copynorm. The contributors include a broad range of leading and agenda-setting scholars, including as N. Katherine Hayles, Wendy Hui Kyong Chun, Johanna Drucker, Lisa Gitelman, Safiya Noble, Sarah T. Roberts and Nicole Starosielski.
Uncertainty is inherent to archival practices; the archive as a site of knowledge is fraught with unknowns, errors, and vulnerabilities that are present, and perhaps even amplified, in big data regimes. Bringing lessons from the study of the archive to bear on big data, the contributors consider the broader implications of big data's large-scale determination of knowledge.