Data Justice, Computational Social Science and Policy

Abstract

Big data has increased attention to Computational Social Science (CSS) on the part of policymakers because it has the power to make populations, activities and behaviour visible in ways that were not previously possible. This kind of analysis, however, often has unforeseen implications for those who are the subjects of the research. This chapter asks what a social justice perspective can tell us about the potential, and the risks, of this kind of analysis when it is oriented towards informing policy. Who benefits, and how, when computational methods and new data sources are used to conduct policy-relevant analysis? Should CSS sidestep, through its novelty and its identification with computational and statistical methodologies, sidestep ethical review and the assessments of power asymmetries and methodological justification that are common in social science research? If not, how should these be applied to CSS research, and what kind of assessment is appropriate? The analysis offers two main conclusions: first, that the field of CSS has evolved without an accompanying evolution of debates on ethics and justice and that these debates are long overdue. Second, that CSS is privileged as policy-relevant research precisely because of many of the features which bring up concerns about justice-large-scale datasets, remote data gathering, purely quantitative methods and an orientation towards policy questions rather than the needs of the research subjects.