Feminist Data Studies: Using Digital Methods for Ethical, Reflexive and Situated Socio-Cultural Research

Abstract

What could a social-justice oriented, feminist data studies look like? The current datalogical turn foregrounds the digital datafication of everyday life, increasing algorithmic processing and data as an emergent regime of power/knowledge. Scholars celebrate the politics of big data knowledge production for its omnipotent objectivity or dismiss it outright as data fundamentalism that may lead to methodological genocide. In this feminist and postcolonial intervention into gender-, race- and geography-blind ‘big data’ ideologies, I call for ethical, anti-oppressive digital data-driven research in the social sciences and humanities. I argue that a reflexive data scholarship can emerge from the reintegration of feminist and postcolonial science studies and ethics of care ideals. Although it is not a panacea for all ails of data mining, I offer a road map for an alternative data-analysis practice that is more power-sensitive and accountable. By incorporating a people-centric and context-aware perspective that acknowledges relationships of dependency, reflects on temptations, and scrutinises benefits and harm, an ‘asymmetrically reciprocal’ (Young, 1997) research encounter may be achieved. I bring this perspective to bear on experiences of a two-year research project with eighty-four young Londoners on digital identities and living in a highly diverse city. I align awareness of uneven relations of power and knowledge with the messy relation of dependency between human and non-human actors in data analysis. This framework productively recognises that digital data cannot be expected to speak for itself, that data do not emerge from a vacuum, and that isolated data patterns cannot be the end-goal of a situated and reflexive research endeavor. Data-driven research, in turn, shows the urgency for renewed feminist ethical reflection on how digital mediation impacts upon responsibility, intersectional power relations, human subjectivity and the autonomy of research participants over their own data.