Big Data Surveillance: The Case of Policing

By Sarah Brayne. American Sociological Review, 2017

619 citations recorded by the source corpus.

This article examines the intersection of two structural developments: the growth of surveillance and the rise of “big data.” Drawing on observations and interviews conducted within the Los Angeles Police Department, I offer an empirical account of how the adoption of big data analytics does—and does not—transform police surveillance practices. I argue that the adoption of big data analytics facilitates amplifications of prior surveillance practices and fundamental transformations in surveillance activities. First, discretionary assessments of risk are supplemented and quantified using risk scores. Second, data are used for predictive, rather than reactive or explanatory, purposes. Third, the proliferation of automatic alert systems makes it possible to systematically surveil an unprecedentedly large number of people. Fourth, the threshold for inclusion in law enforcement databases is lower, now including individuals who have not had direct police contact. Fifth, previously separate data systems are merged, facilitating the spread of surveillance into a wide range of institutions. Based on these findings, I develop a theoretical model of big data surveillance that can be applied to institutional domains beyond the criminal justice system. Finally, I highlight the social consequences of big data surveillance for law and social inequality.

Subfields: Information Systems, Political Science and International Relations, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/e59d5ee879e408cccc7c055e44650dcb6f88c86f

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Citations in the corpus

The Principles and Limits of Algorithm-in-the-Loop Decision Making (2019, background): “Ethnographic studies have documented how the uses of algorithms in practice can di er signi cantly from the planned and proclaimed uses, with algorithms often being ignored or resisted by those charged with using them [7, 11].”
Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selection (2022, background): “Moreover, tracking demographic variables and other author metadata, if deployed at scale, implies a certain level of invasive surveillance (Brayne, 2017).”
Does Predictive Policing Lead to Biased Arrests? Results From a Randomized Controlled Trial (2018, background): “…and concerns have been raised by several national organizations (for example the ACLU and NAACP) (https://www.aclu.org/other/statement-concern-about-predictive-policing-aclu-and-16-civil-rights-privacy-racial-justice) and recent research articles (Brayne 2017; Jefferson 2017; Ferguson, in press).”
“The Computer Said So”: On the Ethics, Effectiveness, and Cultural Techniques of Predictive Policing (2018, background): ““These social dynamics inform the historical crime data that are fed into the predictive policing algo-rithm,” she (Brayne 2017, p. 998) notes.”
Content moderation, AI, and the question of scale (2020, background): “…automating moderation, many of which resonate with familiar concerns about AI and data science more broadly, and that animate worries about automated data-driven insurance assessments, hiring software, and automated medical diagnostics (Ajunwa, 2020; Benjamin, 2019; Brayne, 2017; Eubanks, 2018).”
The Society of Algorithms (2021, background): “…showing that professionals outfitted with algorithmic tools also routinely express skepticism and override or ignore them in their day-to-day work (Brayne 2017, 2020; Christin 2017, 2020; O’Neill 2016).8 Finally, algorithmic systems and their advocates run the risk of propelling the dangerous…”
Getting Eyes in the Home: Child Protective Services Investigations and State Surveillance of Family Life (2020, background): “With quantified risk assessments based in part on the number of prior investigations, substantiated or not (Hirschman and Bosk 2019), stratified surveillance accumulates to exacerbate inequality (Brayne 2017; Eubanks 2018).”
Algorithms at Work: The New Contested Terrain of Control (2020, background): “For example, Brayne (2017) details how police organizations used “ predictive policing ” algorithms to identify “ high-risk ” individuals and places, and employed these to direct enforcement officials ’ inspection priorities.”
AI Ethics in Predictive Policing: From Models of Threat to an Ethics of Care (2019, neutral): “Recent approaches utilize “big data” techniques and arguably entail forms of mass surveillance of the public [24].”
People’s Councils for Ethical Machine Learning (2018, background): “If those machinic judgments are then used to target future activity, we have the technological reproduction of social discrimination (Brayne, 2017).”
Data in New Delhi's predictive policing system (2020, methodology): “Our research builds on existing work [3, 24] that focuses on ex-ante assessment of predictive policing systems like CMAPS.”
Ethics as Methods: Doing Ethics in the Era of Big Data Research—Introduction (2018, background): “This in turn can influence the resources certain people have access to, how likely they are to succeed at various endeavors, or how likely they are to be penalized by governmental agencies (see also Brayne, 2017).”
Towards Transparency by Design for Artificial Intelligence (2020, background): “…such as AI-based diagnostic tools for health applications (e.g., detection of diabetic retinopathy, cf. Abràmoff et al. 2018), recommender systems (e.g., YouTube recommender algorithms, cf. Bishop 2018), or predictive policing and criminal sentencing (Brayne 2017; Brayne and Christin 2020).”