TY - JOUR
T1 - Tracking a critical look at the critical turn in data science
T2 - From “data feminism” to transnational feminist data science
AU - Tacheva, Zhasmina
N1 - Funding Information:
The author received no financial support for the research, authorship, and/or publication of this article.
Publisher Copyright:
© The Author(s) 2022.
PY - 2022/7
Y1 - 2022/7
N2 - 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.
AB - 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.
KW - Data
KW - critical algorithm studies
KW - critical data studies
KW - feminism
KW - postcolonial studies
KW - transnational feminism
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U2 - 10.1177/20539517221112901
DO - 10.1177/20539517221112901
M3 - Article
AN - SCOPUS:85134640559
SN - 2053-9517
VL - 9
JO - Big Data and Society
JF - Big Data and Society
IS - 2
ER -