Criminal Futures: Predictive Policing and Everyday Police Work

Egbert, Simon; Leese, Matthias

doi:10.21428/cb6ab371.17e3e7ab

Cited by 24 publications

(29 citation statements)

References 81 publications

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“…A more productive way of engaging with this kind of accuracy would be to understand it as a marker or proxy for problematic inequalities, such as racism, in a society and then the goal should be to actually reduce this accuracy to increase the agency of marginalized people to have different futures. For example, in predictive policing, the use of available data that appears to be predictable of crime as defined by police can be enticing also as it could be used to produce seemingly accurate algorithms and adhere to logics and practices already present in policing (Egbert and Leese 2021). Yet, this accuracy again also depends on the production of ignorance on non-carceral futures not visible in data collected in an unequal present that does not consider other futures.…”

Section: Accuracy Is Constructed By Selecting Contingent Predictable ...mentioning

confidence: 99%

Constructing Certainty in Machine Learning: On the performativity of testing and its hold on the future

Grill¹

2022

Preprint

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The use of opaque machine learning algorithms is often justified by their accuracy. For example, IBM has advertised its algorithms as being able to predict when workers will quit with 95% accuracy, an EU research project on lie detection in border control has reported 75% accuracy, and researchers have claimed to be able to deduce sexual orientation with 91% accuracy from face images. Such performance numbers are, on the one hand, used to make sense of the functioning of opaque algorithms and promise to quantify the quality of algorithmic predictions. On the other hand, they are also performative, rhetorical, and meant to convince others of the ability of algorithms to know the world and its future objectively, making calculated, partial visions appear certain. This duality marks a conflict of interest when the actors who conduct an evaluation also profit from positive outcomes. Building on work in the sociology of testing and agnotology, I discuss seven ways how the construction of high accuracy claims also involves the production of ignorance. I argue that this ignorance should be understood as productive and strategic as it is imbued with epistemological authority by making uncertain matters seem certain in ways that benefit some groups over others. Several examples illustrate how tech companies increasingly strategically produce ignorance reminiscent of tactics used by controversial companies with a high concentration of market power such as big oil or tobacco. My analysis deconstructs claims of certainty by highlighting the politics and contingencies of testing used to justify the adoption of algorithms. I further argue that current evaluation practices in ML are prone to producing problematic forms of ignorance, like misinformation, and reinforcing structural inequalities due to how human judgment and power structures are invisibilized, narrow, oversimplified metrics overused, and pernicious incentive structures encourage overstatements enabled by flexibility in testing. I provide recommendations on how to deal with and rethink incentive structures, testing practices, and the communication and study of accuracy with the goal of opening possibilities, making contingencies more visible, and enabling the imagination of different futures.

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Section: Accuracy Is Constructed By Selecting Contingent Predictable ...mentioning

confidence: 99%

Constructing Certainty in Machine Learning: On the performativity of testing and its hold on the future

Grill¹

2022

Preprint

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“…Man könnte aber auch sures. The most important aspect of predictive policing is, in this sense, that it minimizes the time period between data collection, analysis, and the production of actionable intelligence, meaning that operational crime prevention measures can be implemented more quickly and in a more targeted fashion.« Simon Egbert, Matthias Leese (2020)…”

Section: Environmentale Forensik 177mentioning

confidence: 99%

Medien der Forensik

Rothöhler¹

2021

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“…In particular, the phenomenon of predictive policing, along with other modern methods of crime prevention and prosecution such as data mining and face recognition technology, are rapidly gaining attention (Brayne 2021;Egbert and Leese 2021;Egbert and Krasmann 2020;Kotsoglou and Oswald 2020;Benbouzid 2019;Završnik 2019;Egbert 2018;Yu et al 2011). Predictive policing tools promise to bring about a substantial revolution in policing and claim to be shaping the future of criminal justice.…”

Section: Introductionmentioning

confidence: 99%

Smart criminal justice: exploring the use of algorithms in the Swiss criminal justice system

et al. 2022

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In the digital age, the use of advanced technology is becoming a new paradigm in police work, criminal justice, and the penal system. Algorithms promise to predict delinquent behaviour, identify potentially dangerous persons, and support crime investigation. Algorithm-based applications are often deployed in this context, laying the groundwork for a ‘smart criminal justice’. In this qualitative study based on 32 interviews with criminal justice and police officials, we explore the reasons why and extent to which such a smart criminal justice system has already been established in Switzerland, and the benefits perceived by users. Drawing upon this research, we address the spread, application, technical background, institutional implementation, and psychological aspects of the use of algorithms in the criminal justice system. We find that the Swiss criminal justice system is already significantly shaped by algorithms, a change motivated by political expectations and demands for efficiency. Until now, algorithms have only been used at a low level of automation and technical complexity and the levels of benefit perceived vary. This study also identifies the need for critical evaluation and research-based optimization of the implementation of advanced technology. Societal implications, as well as the legal foundations of the use of algorithms, are often insufficiently taken into account. By discussing the main challenges to and issues with algorithm use in this field, this work lays the foundation for further research and debate regarding how to guarantee that ‘smart’ criminal justice is actually carried out smartly.

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Criminal Futures: Predictive Policing and Everyday Police Work

Cited by 24 publications

References 81 publications

Constructing Certainty in Machine Learning: On the performativity of testing and its hold on the future

Constructing Certainty in Machine Learning: On the performativity of testing and its hold on the future

Medien der Forensik

Smart criminal justice: exploring the use of algorithms in the Swiss criminal justice system

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