Abstract Patterns are the epistemological core of predictive policing. With the move towards digital prediction tools, the authority of the pattern is rearticulated and reinforced in police work. Based on empirical research about predictive policing software and practices, this article puts the authority of patterns into perspective. Introducing four ideal-typical styles of pattern identification, we illustrate that patterns are not based on a singular logic, but on varying rationalities that give form to and formalize different understandings about crime. Yet, patterns render such different modes of reasoning about crime, and the way in which they feed back into policing cultures, opaque. Ultimately, this invites a stronger reflection about the political nature of patterns.
Predictive Policing and the Politics of Patterns
Abstract. Patterns are the epistemological core of predictive policing. With the move towards digital prediction tools, the authority of the pattern is rea
Predictive Policing Explained
Attempts to forecast crime with algorithmic techniques could reinforce existing racial biases in the criminal justice system.
Prediction as extraction of discretion
I argue that prediction is not primarily a technological means for knowing future outcomes, but a social model for extracting and concentrating discretionary power. Prediction is a ‘relational grammar’ that governs this allocation of discretion: the everyday ability to define one's situation. This extractive dynamic extends a long historical pattern, in which new methods for producing knowledge entail a redistribution of decision-making power. I focus on two contemporary domains: (1) crime and policing are emblematic of how predictive systems are extractive by design, with pre-existing interests governing what is measured and what persistently goes unmeasured. (2) The prediction of productivity demonstrates the long tradition of extracting discretion as a means to extract labour power. Time after time, making human behaviour more predictable for the client of prediction (the manager, the police officer) often means making life and work more unpredictable for the target of prediction (the employee, the urban citizen).
Prediction as extraction of discretion
I argue that prediction is not primarily a technological means for knowing future outcomes, but a social model for extracting and concentrating discretionary power. Prediction is a ‘relational grammar’ that governs this allocation of discretion: the everyday ability to define one's situation. This extractive dynamic extends a long historical pattern, in which new methods for producing knowledge entail a redistribution of decision-making power. I focus on two contemporary domains: (1) crime and policing are emblematic of how predictive systems are extractive by design, with pre-existing interests governing what is measured and what persistently goes unmeasured. (2) The prediction of productivity demonstrates the long tradition of extracting discretion as a means to extract labour power. Time after time, making human behaviour more predictable for the client of prediction (the manager, the police officer) often means making life and work more unpredictable for the target of prediction (the employee, the urban citizen).
Artificial Intelligence, Predictive Policing, and Risk Assessment for Law Enforcement
There are widespread concerns about the use of artificial intelligence in law enforcement. Predictive policing and risk assessment are salient examples. Worries include the accuracy of forecasts that guide both activities, the prospect of bias, and an apparent lack of operational transparency. Nearly breathless media coverage of artificial intelligence helps shape the narrative. In this review, we address these issues by first unpacking depictions of artificial intelligence. Its use in predictive policing to forecast crimes in time and space is largely an exercise in spatial statistics that in principle can make policing more effective and more surgical. Its use in criminal justice risk assessment to forecast who will commit crimes is largely an exercise in adaptive, nonparametric regression. It can in principle allow law enforcement agencies to better provide for public safety with the least restrictive means necessary, which can mean far less use of incarceration. None of this is mysterious. Nevertheless, concerns about accuracy, fairness, and transparency are real, and there are tradeoffs between them for which there can be no technical fix. You can't have it all. Solutions will be found through political and legislative processes achieving an acceptable balance between competing priorities.
Artificial Intelligence, Predictive Policing, and Risk Assessment for Law Enforcement
There are widespread concerns about the use of artificial intelligence in law enforcement. Predictive policing and risk assessment are salient examples. Worries include the accuracy of forecasts that guide both activities, the prospect of bias, and an apparent lack of operational transparency. Nearly breathless media coverage of artificial intelligence helps shape the narrative. In this review, we address these issues by first unpacking depictions of artificial intelligence. Its use in predictive policing to forecast crimes in time and space is largely an exercise in spatial statistics that in principle can make policing more effective and more surgical. Its use in criminal justice risk assessment to forecast who will commit crimes is largely an exercise in adaptive, nonparametric regression. It can in principle allow law enforcement agencies to better provide for public safety with the least restrictive means necessary, which can mean far less use of incarceration. None of this is mysterious. Nevertheless, concerns about accuracy, fairness, and transparency are real, and there are tradeoffs between them for which there can be no technical fix. You can't have it all. Solutions will be found through political and legislative processes achieving an acceptable balance between competing priorities.
Does Predictive Policing Lead to Biased Arrests? Results From a Randomized Controlled Trial
Racial bias in predictive policing algorithms has been the focus of a number of recent news articles, statements of concern by several national organizations (e.g., the ACLU and NAACP), and simulat...
Does Predictive Policing Lead to Biased Arrests? Results From a Randomized Controlled Trial
Racial bias in predictive policing algorithms has been the focus of a number of recent news articles, statements of concern by several national organizations (e.g., the ACLU and NAACP), and simulat...
Predictive policing AI is on the rise − making it accountable to the public could curb its harmful effects
AI that anticipates where crimes are likely to occur and who might commit them has a troubling track record. Democratic accountability could shine a light on the technology and how it’s used.
Predictive policing AI is on the rise − making it accountable to the public could curb its harmful effects
AI that anticipates where crimes are likely to occur and who might commit them has a troubling track record. Democratic accountability could shine a light on the technology and how it’s used.
Going beyond the “common suspects”: to be presumed innocent in the era of algorithms, big data and artificial intelligence
This article explores the trend of increasing automation in law enforcement and criminal justice settings through three use cases: predictive policing, machine evidence and recidivism algorithms. The focus lies on artificial-intelligence-driven tools and technologies employed, whether at pre-investigation stages or within criminal proceedings, in order to decode human behaviour and facilitate decision-making as to whom to investigate, arrest, prosecute, and eventually punish. In this context, this article first underlines the existence of a persistent dilemma between the goal of increasing the operational efficiency of police and judicial authorities and that of safeguarding fundamental rights of the affected individuals. Subsequently, it shifts the focus onto key principles of criminal procedure and the presumption of innocence in particular. Using Article 6 ECHR and the Directive (EU) 2016/343 as a starting point, it discusses challenges relating to the protective scope of presumption of innocence, the burden of proof rule and the in dubio pro reo principle as core elements of it. Given the transformations law enforcement and criminal proceedings go through in the era of algorithms, big data and artificial intelligence, this article advocates the adoption of specific procedural safeguards that will uphold rule of law requirements, and particularly transparency, fairness and explainability. In doing so, it also takes into account EU legislative initiatives, including the reform of the EU data protection acquis, the E-evidence Proposal, and the Proposal for an EU AI Act. Additionally, it argues in favour of revisiting the protective scope of key fundamental rights, considering, inter alia, the new dimensions suspicion has acquired.
Going beyond the “common suspects”: to be presumed innocent in the era of algorithms, big data and artificial intelligence
This article explores the trend of increasing automation in law enforcement and criminal justice settings through three use cases: predictive policing, machine evidence and recidivism algorithms. The focus lies on artificial-intelligence-driven tools and technologies employed, whether at pre-investigation stages or within criminal proceedings, in order to decode human behaviour and facilitate decision-making as to whom to investigate, arrest, prosecute, and eventually punish. In this context, this article first underlines the existence of a persistent dilemma between the goal of increasing the operational efficiency of police and judicial authorities and that of safeguarding fundamental rights of the affected individuals. Subsequently, it shifts the focus onto key principles of criminal procedure and the presumption of innocence in particular. Using Article 6 ECHR and the Directive (EU) 2016/343 as a starting point, it discusses challenges relating to the protective scope of presumption of innocence, the burden of proof rule and the in dubio pro reo principle as core elements of it. Given the transformations law enforcement and criminal proceedings go through in the era of algorithms, big data and artificial intelligence, this article advocates the adoption of specific procedural safeguards that will uphold rule of law requirements, and particularly transparency, fairness and explainability. In doing so, it also takes into account EU legislative initiatives, including the reform of the EU data protection acquis, the E-evidence Proposal, and the Proposal for an EU AI Act. Additionally, it argues in favour of revisiting the protective scope of key fundamental rights, considering, inter alia, the new dimensions suspicion has acquired.
Police Abolition | The Paper Pilot
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How Police Make Up The Law (ft. LegalEagle)