Big Data, Algorithmic Governmentality and the Regulation of Pandemic Risk

被引:26
作者
Roberts, Stephen L.
机构
基金
加拿大自然科学与工程研究理事会;
关键词
PUBLIC-HEALTH SURVEILLANCE; TECHNOLOGIES; SECURITY; MEDIA;
D O I
10.1017/err.2019.6
中图分类号
D9 [法律]; DF [法律];
学科分类号
0301 ;
摘要
This article investigates the rise of algorithmic disease surveillance systems as novel technologies of risk analysis utilised to regulate pandemic outbreaks in an era of big data. Critically, the article demonstrates how intensified efforts towards harnessing big data and the application of algorithmic processing techniques to enhance the real-time surveillance and regulation infectious disease outbreaks significantly transform practices of global infectious disease surveillance; observed through the advent of novel risk rationalities which underpin the deployment of intensifying algorithmic practices to increasingly colonise and patrol emergent topographies of data in order to identify and govern the emergence of exceptional pathogenic risks. Conceptually, this article asserts further howthe rise of these novel risk regulating technologies within a context of big data transforms the government and forecasting of epidemics and pandemics: illustrated by the rise of emergent algorithmic governmentalties of risk within contemporary contexts of big data, disease surveillance and the regulation of pandemic.
引用
收藏
页码:94 / 115
页数:22
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