Unintended Consequences of Data-driven public participation: How Low-Traffic Neighborhood planning became polarized

Abstract

This paper examines how data-driven consultation contributes to dynamics of political polarization, using the case of ‘Low-Traffic Neighborhoods’ in London, UK. It explores how data-driven consultation can facilitate participation, including ‘agonistic data practices” (Crooks and Currie, 2022) that challenge the dominant interpretations of digital data. The paper adds empirical detail to previous studies of agonistic data practices, concluding that agonistic data practices require certain normative conditions to be met, otherwise dissenting data practices can contribute to dynamics of polarization. The results of this paper draw on empirical insights from the political context of the UK to explain how ostensibly democratic processes including data-driven consultation establish some kinds of knowledge as more legitimate than others. Apparently ‘objective’ knowledge, or calculable data, is attributed greater legitimacy than strong feelings or affective narratives. This can displace affective responses to policy decisions into insular social media spaces where polarizing dynamics are at play. Affective polarization, where political difference is solidified through appeals to feeling, creates political distance and the dehumanization of ‘others’. This can help to amplify conspiracy theories that pose risks to democracy and to the overall legitimacy of media environments. These tendencies are exacerbated when processes of consultation prescribe narrow or specific contributions, valorize quantifiable or objective data and create limited room for dissent.

Keywords: datafication, governance, agonism, social media, polarization, co-production