The Algorithmic Shift: Theorizing the Social Implications of Big Data and Crowdsourcing

The rapid rise to prominence of big data and crowdsourcing in the human sciences and the social world at large are completely transforming both our understanding of this world and our ways of studying it. However, this algorithmic shift has barely begun to be theorized within sociology—something that this paper aims to rectify by focusing on two specific themes. Firstly, the paper examines how the algorithmic shift affects what kind of knowledge is socially and institutionally valued and how this knowledge is produced. Big data is geared toward quantitative and positivist methodologies and epistemologies instead of their qualitative and critical-interpretive counterparts, while crowdsourcing redefines expertise and valuation away from a formal and hierarchical set of norms and practices exercised by a cadre of peer-consecrated specialists to an informal and horizontal aggregation of lay opinions and collaborative skills (the so-called ‘wisdom of the crowd’). Secondly, the paper considers how the algorithmic shift fundamentally alters our conceptions of the social, since its logic frames society as the mere aggregation of measurable individual tendencies and choices—thereby leaving behind any notion of collective or structural forces. Moreover, this same logic understands agency as a matter of calculated probabilities amongst a range of possible courses of action and decisions by the actor. Throughout the paper, these arguments are illustrated by drawing upon recent public controversies involving the use of big data and crowdsourcing to tackle global problems (climate change, gender-based violence, famines, mass atrocities, etc.). By analyzing these controversies as terrains upon which global civil society actors are debating the use of an algorithmic logic to put into play, negotiate, and reorient the conventional meanings of criteria of accuracy, verifiability, comprehensiveness and selectivity of knowledge, we can grasp more clearly and fully the social implications of big data and crowdsourcing today.