Uncertain Archives: Approaching the Unknowns, Errors and Vulnerabilities of Big Data through Cultural Theories of the Archive

Research output: Contribution to journalJournal articleResearchpeer-review


From global search engines to local smart cities, from public health monitoring to personal self-tracking technologies, digital technologies continuously capture, process, and archive social, material, and affective information in the form of big data. Although the use of big data emerged from the human desire to acquire more knowledge and master more information and to eliminate human error in large-scale information management, it has become clear in recent years that big data technologies, and the archives of data they accrue, bring with them new and important uncertainties in the form of new biases, systemic errors, and, as a result, new ethical challenges that require urgent attention and analysis. This collaboratively written article outlines the conceptual framework of the Uncertain Archives research collective to show how cultural theories of the archive can be meaningfully applied to the empirical field of big data. More specifically, the article argues that this approach grounded in cultural theory can help research going forward to attune to and address the uncertainties present in the storage and analysis of large amounts of information. By focusing on the notions of the unknown, error, and vulnerability, we reveal a set of different, albeit intertwined, configurations of archival uncertainty that emerge along with the phenomenon of big data use. We regard these configurations as central to understanding the conditions of the digitally networked data archives that are a crucial component of today's cultures of surveillance and governmentality.
Original languageEnglish
JournalSurveillance & Society
Issue number3/4
Pages (from-to)422-441
Publication statusPublished - 9 Sep 2019

    Research areas

  • Faculty of Humanities - archive theories, big data, algorithms, feminist theories, Digital media

Number of downloads are based on statistics from Google Scholar and www.ku.dk

No data available

ID: 210363746