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engaging the public in developing policies

Early commentary on the social justice implications of the big data age tended to oscillate between celebratory and cautionary: celebration focused on bringing formerly invisible clusters of inequality and injustice to light; caution dwelled instead on the loss of control that comes from individuals being turned into data points and hence into governable subjects (US PCAST, 2014)—through unwitting transfers of personal information to big corporations, through invasions of privacy or errors of classification by public and private institutions of governmentality (Foucault, 1978), and through algorithms whose accuracy and lawfulness are not open to public questioning. In this paper, I take a somewhat different tack, asking not whether data are always reliable as reported (clearly, they are not), nor whether modes of data collection and interpretation can give rise to injustice claims (clearly, they can), but rather about the epistemic presumptions that shape the making of data in the first place. This, then, is not an inquiry into the validity of particular data claims or correlations. It is a more foundational exploration of how power works in rerepresenting things that happen in the world in the form of data points, data sets, or data associations.

Any form of data collection involves, to begin with, an act of seeing and recording something that was previously hidden and possibly nameless. Random observations do not add up to data; the aggregated incidences represented by a data set have to have meaning, as standing for a classifiable, coherent phenomenon in the world. At the same time, for data to have an impact on law and policy, information must be seen as actionable, that is, numbers or other quantitative representations, such as charts and graphs, must show people both something actual and something that begs to be investigated, explained, or solved. In short, if a data set is to elicit a social response, knowledge of something that matters and principles for understanding why it matters must be generated together, or coproduced (Jasanoff, 2004). Put still differently, to be actionable, data must be seen as problematizing the taken-for-granted order of society (on problematization, see Foucault, 1998): by pointing to questions or imbalances that can, in the ideal case, be corrected or rectified, or simply better understood, through the systematic compilation of occurrences, frequencies, distributions, or correlations that make up a meaningful data set.