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Friday, October 7 • 11:00 - 12:30
Fem Big Data I

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What would feminist big data, data studies and datavis look like? 1 VISIONS
Helen Kennedy (chair/submitter) (1), Jean Burgess (2), Kate Crawford (3), Aristea Fotopoulou (4), Rosemary Lucy Hill (5), Kate O'Riordan (6)
1: University of Sheffeld, UK; 2: QUT, Australia; 3: Microsoft, USA; 4: University of Brighton, UK; 5: University of Leeds, UK; 6: University of Sussex, UK

This is a proposal for two interlinked roundtable events focused on how feminism might do big data, data studies and data visualisation. The first focuses on visions and imagining of feminist big data futures. The second on concrete big data and datavis practices in the present, that might be considered to be feminist or as models for feminist approaches. Each starts with a series of short presentations from panelists, and moves on to an open discussion of the question: what would feminist big data, data studies and datavis look like? All of the ten named participants will be present at and will contribute to both roundtables, with five giving short presentations in each.
How can and should feminists respond to the rise of big data? Given that critique of the assumed objectivity and neutrality of big data and related methods has a feminist history, feminist scholars are well-placed to respond to the problems that big data usher forth. One outcome of objectivity critique is a heavy reliance on qualitative methods in feminist research, yet it is precisely because of the types of problems that feminist scholarship has been so good at identifying that there is a need not just for feminist critiques of quantitative methods, data and assumptions of objectivity, but for feminism to do big data and data visualisation. In other words, we need feminist data studies which is active in creating, representing and communicating data. How do we move forward from critiques of data as not really objective, but cooked, to understanding how and why it matters to feminists and feminism? How do we respond to Haraway’s proposal that encoding and visualisation are inherently patriarchal projects? What might feminist big data, data studies and datavis look like?


Helen Kennedy

University of Sheffeld, United Kingdom


Jean Burgess

Digital Media Research Centre, Queensland University of Technology, Australia

Kate Crawford

Microsoft, USA

Aristea Fotopoulou


Rosemary Lucy Hill

University of Leeds, UK

Kate O'Riordan

University of Sussex, United Kingdom

Friday October 7, 2016 11:00 - 12:30 CEST
HU 1.205 Humboldt University of Berlin Dorotheenstr. 24