Predictable policing: New technology, old bias, and future resistance in big data surveillance

Abstract

Within this article, we explore the rise of predictive policing in the United States as a form of big data surveillance. Bringing together literature from communication, criminology, and science and technology studies, we use a case study of Milwaukee, Wisconsin, USA to outline that predictive policing, rather than being a novel development, is in fact part of a much larger, historical network of power and control. By examining the mechanics of these policing practices: the data inputs, behavioral outputs, as well as the key controllers of these systems, and the individuals who influenced their adoption, we show that predictive policing as a form of big data surveillance is a sociotechnical system that is wholly human-constructed, biases and all. Identifying these elements of the surveillance network then allows us to turn our attention to the resistive practices of communities who historically and presently live under surveillance – pointing to the types of actions and imaginaries required to combat the myth and allure that swirls around the rhetoric of big data surveillance today.

Imagining Impact in Global Supply Chains: Data-Driven Sustainability and the Production of Surveillable Space

“…Building on these interdisciplinary insights, the notion of surveillable space developed in this article seeks to shift attention from particular (in the sense of both specific and seemingly disconnected) sites of surveillance to the work that goes into legitimizing the surveillance of those sites, thereby emphasizing the spatiality of what Haggerty and Ericson (2000) have theorized as the "surveillant assemblage" and extending Thrift and French's (2002: 331) theory of the "automatic production of space," which deals with the way software-as-a-technology-of-ordering has "[become] one of the chief ways of animating space." Examining these dynamics in the context of global agrocommodity supply chains originating in the Global South enriches critical theories of surveillance and space, which tend to be empirically grounded in Europe and North America, with a particular focus on smart cities (e.g., Galdon-Clavell 2013) and policing (e.g., Sewell, Jefferson, and Lee 2016;Benjamin 2016;Minocher and Randall 2020). 2…”

Why government supported smart city initiatives fail: Examining community risk and benefit agreements as a missing link to accountability for equity-seeking groups

“…For over-policed and over-surveilled communities, institutions of government, particularly law enforcement agencies, have failed these communities for decades. For these equity-seeking communities the implementation of smart city projects is often a technology-based extension of existing practices which exacerbates existing inequalities rather than mitigating them (Joh, 2019;Minocher and Randall, 2020;O'Malley and Smith, 2022;Qarri and Gill, 2022).…”

“…For example, to resist datafication and surveillance risks, communities need strong political organizing at the local level, as well as the ability to conduct monitoring and evaluation. As an example of local political organizing, Minocher and Randall (2020) describe the importance of local community groups in achieving bans on facial recognition technology used in U.S. cities. As an example of the ability to perform independent monitoring and evaluation, Amnesty International (2021) completed a crowdsourced project identifying 15,000 surveillance cameras in New York City, but these surveillance cameras are not equitably distributed across the city; the surveillance cameras are concentrated in Black and Brown neighborhoods.…”

Digital culture meets data: Critical perspectives

“…Being framed as impartial and objective, sociotechnical systems of policing in fact have historical biases encoded within their operation. Minocher and Randall (2020) finally highlight the potential of developing social bonds and networks as forms of community resistance to big data surveillance and its sociotechnical networks of power. Indeed, it is not enough to note how bias and discrimination can be perpetuated with the use of data technologies.…”

“…Using Milwaukee in Wisconsin as a case study, Minocher and Randall (2020) discuss how the technological imaginaries around predictive policing obscure the dangerous practices associated with the technology, especially when its use is linked to the surveillance of marginalized social groups, such as Black and Brown communities residing in certain geographical areas. The authors stress these processes have real-life consequences, such as decisions for placing children into care or housing homeless people.…”