The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement
By P. Manning. Contemporary Sociology, 2019
359 citations recorded by the source corpus.
The use of big data by the police is part of the very broad discussion of the use of big data in every aspect of our lives. It is the newest member of a small club—whose founding members were death and taxes—of things we cannot avoid. Even before the scandals concerning the alleged meddling of foreign governments in our elections and the revelations about Facebook’s targeted and politicized ads, we all really knew that we were being watched and monitored carefully. The interface between the Internet and GPS pretty much reveals our life story. And people who somehow find a way to disappear from the grid become persons of interest for precisely this reason. It’s why we should think twice about buying an airline ticket with cash. Trying to understand these issues is also a reminder that the day of discrete academic disciplines has gone. Andrew Ferguson’s The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement has to be a multidisciplinary effort to explain something that is part social science, part engineering, and part law. It also resonates for sociologists, particularly sociological theorists, steeped in Michel Foucault’s work. He famously declared that around 1800 a new ‘‘episteme’’ emerged—a new set of institutions, ideas, and technologies that ushered in a new era of discipline and surveillance. Foucault’s appropriation of Bentham’s Panopticon led him to coin the term ‘‘panopticism’’ that anticipated many of the themes that inform Ferguson’s research. For Foucault, controlling criminals through extensive monitoring led all too easily to the control of entire populations of the assumed innocent and even to the people doing the monitoring itself. Ferguson provides us with an up-to-date, high-tech Foucault packaged in an engaging and timely book. As he makes clear in the early chapters, he is very aware of the general issues of big data and mundane surveillance, despite his primary interest in police practices. To this end, Ferguson distinguishes four issues: black, blue, bright, and no data. By ‘‘black data,’’ Ferguson not only highlights the racial inequities and legal concerns that are part of these new policing technologies but also the opacity of the technology itself. That is, the algorithms and computer programs that constitute big data policing are so complicated that accountability is compromised. As Ferguson puts it, ‘‘Policing is not a field that typically requires PhD-level technological expertise’’ (p. 136). Although Ferguson tacitly accepts the technical difficulty of the material, I wondered whether he could have done more to confront the technological opacity of black data. After all, molecular biology is also a tremendously difficult field, but there are ways of making its discoveries accessible. ‘‘Blue data’’ are quite different: they collect information about police practices and provide oversight. Efforts to police the police are the hallmark of blue data, and Ferguson credits the Obama administration with the creation of a collaboration between interested parties that has led to 90 data sets drawn from 50 local jurisdictions (p. 166). Advocates of ‘‘bright data’’ hope that big data can be used to make social programs more efficient by identifying and anticipating risk. Thus, a mother who is identified as someone who is struggling to feed her family could be sent information about food pantries. Ferguson also confronts the twin problems of missing data and expensive data. He estimates that at best big data policing makes use of only 50 percent of crime data and acknowledges that this opens the door to distortions and mistakes (p. 181). Ferguson also recognizes that both the costs and cost-benefits of big data are critical. However, here I was surprised by how little attention the topic actually received—just a page in the middle of the discussion of data concerns in Chapter Ten. Earlier Ferguson had noted very elegantly that ‘‘To find a guilty needle in the haystack, you have to collect a lot of innocent hay’’ (p. 110), and of course this raises the question 170 Reviews
Subfields: Information Systems, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/0a829289a16ae48837cc2905635435db98bacc76
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems
In Sociology and Political Science
Citations in the corpus
- Accountable Artificial Intelligence: Holding Algorithms to Account (2020, background): “Telling examples of high-stakes applications in public sector decision-making already abound: Algorithmic tools are increasingly relied upon by police departments to predict crime and inform policing decisions (Ferguson 2017).”
- AI Ethics in Predictive Policing: From Models of Threat to an Ethics of Care (2019, background): “Predictive policing has been controversial — as it aspires to prevent crime, it also raises questions of prejudice and precrime and effectively treating individuals and communities as guilty of (future) crimes for acts they have not yet committed and may never commit [21], [22].”
- Ethics of artificial intelligence and robotics (2020, background): “One such use is in ‘predictive policing’ (Programs 2014), which many fear might lead to an erosion of public liberties (Ferguson 2017) because ‘predictive analytics’ can give power to people using it, and take away power from the predicted.”
- Algorithmic realism: expanding the boundaries of algorithmic thought (2020, background): “For example, predictive policing algorithms and risk assessments have been hailed as remedying the injustices of the criminal justice system [43, 53, 117, 123, 143], with signi cant energy spent ensuring that these algorithms satisfy mathematical fairness standards.”