Taking a critical look at the critical turn in data science

From "data feminism" to transnational feminist data science

Bibliographic Data

ID5260423
AuthorsZhasmina Tacheva (0000-0003-3859-5823, Syracuse University, corresponding author)
Year2022
Volume9
Issue2
Publication date2022-07-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueBig Data & Society (JOURNAL)
Journal identifiersISSN: 2053-9517 • E-ISSN: 2053-9517
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1177/20539517221112901
OpenAlexW4285807808
LanguageEN
Citations received10
References cited22

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

Epistemology · Feminism · Feminist philosophy · Feminist theory · Injustice · Oppression · Political science · Politics · Sociology · Computer Science · Ethics and Social Impacts of AI · Feminism, Gender, and Sexuality Studies · Gender Studies · Law · Race, Genetics, and Society

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