Taking a critical look at the critical turn in data science: From "data feminism" to transnational feminist data science. Issue 2 (July 2022)

• Record Type: Journal Article
• Authors: Tacheva, Zhasmina
• Is Part Of: Big data & society. Volume 9:Issue 2(2022)
• Publication Date: 2022-07
• Subjects: Data -- feminism -- transnational feminism -- postcolonial studies -- critical data studies -- critical algorithm studies; Big data -- Social aspects -- Periodicals; Social sciences -- Research -- Data processing -- Periodicals; Social sciences -- Research -- Methodology -- Periodicals; Data mining -- Periodicals; 300.28557
• DOI: 10.1177/20539517221112901
• Languages: English
• ISSNs: 2053-9517

Abstract

Through a critical analysis of recent developments in the theory and practice of data science, including nascent feminist approaches to data collection and analysis, this commentary aims to signal the need for a transnational feminist orientation towards data science. I argue that while much needed in the context of persistent algorithmic oppression, a Western feminist lens limits the scope of problems, and thus—solutions, critical data scholars, and scientists can consider. A resolutely transnational feminist approach on the other hand, can provide data theorists and practitioners with the hermeneutic tools necessary to identify and disrupt instances of injustice in a more inclusive and comprehensive manner. A transnational feminist orientation to data science can pay particular attention to the communities rendered most vulnerable by algorithmic oppression, such as women of color and populations in non-Western countries. I present five ways in which transnational feminism can be leveraged as an intervention into the current data science canon.