Dr Fabio Iapaolo

Focused on city-scale automation, and using self-driving cars (SDCs) as a case study, this article reflects on the role of AI—and in particular, computer vision systems used for mapping and navigation—as a catalyst for urban transformation. Urban research commonly presents AI and cities as having a one-way cause-and-effect relationship, giving undue weight to AI’s impact on cities and overlooking the role of cities in shaping AI. Working at the intersection of data science and social research, this paper aims to counter this trend by exploring the reverse perspective: how do cities affect the development, and expose the present limits, of SDCs? The contribution of this paper is threefold. First, by comparing urban and nonurban environments and thoroughly examining the relationship between computer vision and city-specific sociality and form, it defines machine autonomy/automation as a function of the sociotechnical milieu in which an AI system operates. Second, and related, the paper problematizes the notion of SDCs as autonomous technologies and the role it plays in envisioning contending policy arrangements and technical solutions for achieving full driving automation. Finally, the article offers insight into a materialist and spatialized understanding of AI—namely, not as an abstract quality susceptible to replication within discrete machines, but rather as a distributed property emerging through embodied interactions among a multiplicity of agents (human, non-human, and technological) within/with their environments.

Significant resources and efforts have been devoted, especially in the USA, to develop predictive policing programs. Predictive policing is, at the same time, one of the drivers of the birth, and the ultimate material enactment of, the anticipatory logics that are central to the smart city discourse. Quite surprisingly, however, critical analyses of the smart city have remained divorced from critical criminology and police studies. To fill this gap, this article sets out the first critical, in-depth empirical discussion of Blue CRUSH, a predictive policing program developed in Memphis (TN, USA), where its implementation intersects long-term austerity for urban policy. The article, first, shows that there is no evidence of Blue CRUSH’s capacity to prevent crime, thus adding empirical material to skepticism over the role of predictive policing as a policy solution in the first place. And, second, it argues that, rather than making crime a matter of technological solutions, predictive policing shifts the politics therein – in short, it contributes to the expansion of policing into the field of urban policy at the same time as it disrupts present police work. These takeaways allow to further the critique of the salvific promises implicit in the smart city discourse.