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Accepted Paper:

Data Silence(s): Data Science, Inclusivity, and Barriers to Social Change  
Tonia Sutherland (University of Alabama)

Paper short abstract:

Increasingly there are data science projects that aim to address planet-scale social problems. However, Western and Northern perspectives govern the current data science landscape. This paper discusses the ways underrepresented groups are being further silenced by vagaries in data science practices.

Paper long abstract:

Data work that aspires to global scales is increasingly common in data science. Often collaboratively or collectively undertaken, many of these data science projects aim to address planet-scale social problems and enact social change. What makes these projects all the more fascinating (and provocative) is the thoroughly Western and Northern perspectives that govern the current data science landscape. Even data science teams working on world-historical social and economic inequality thus far have very little data from the Global South. It isn't that these data don't exist. Rather, the issue is that the data science teams, despite the rhetoric of breadth and depth, are frequently narrow when it comes to including anyone from areas of the world beyond highly industrialized, financialized, and densely-networked locales. Whereas in professions like early anthropology, in which data was gathered from elsewhere to make claims about specific groups of others, what we currently see in data science is data gathered around cleanliness and convenience to make claims about everyone everywhere by teams that, at least for now, lack broad representation, inclusivity, and trans-hemispheric participation. Building on this point, this paper discusses the ways underrepresented groups and cultures are being further silenced by vagaries in data science practices - from collection and cleaning to description and representation.

Panel T164
The Potential Futures of Data Science: A Roundtable Intervention
  Session 1 Thursday 1 September, 2016, -