scholarly journals Artificial Intelligence, Predictive Policing, and Risk Assessment for Law Enforcement

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Richard A. Berk
Keyword(s):  
Artificial Intelligence ◽  
Risk Assessment ◽  
Law Enforcement ◽  
Media Coverage ◽  
Annual Review ◽  
Publication Date ◽  
Law Enforcement Agencies ◽  
Apparent Lack ◽  
Predictive Policing ◽  
Competing Priorities

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. Expected final online publication date for the Annual Review of Criminology, Volume 4 is January 13, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Predictive Policing for Reform? Indeterminacy and Intervention in Big Data Policing

2019 ◽  
Vol 17 (3/4) ◽  
pp. 456-472 ◽  
Author(s):  
Aaron Shapiro
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Criminal Justice ◽  
Law Enforcement ◽  
Justice System ◽  
Predictive Analytics ◽  
Law Enforcement Agencies ◽  
Police Reform ◽  
Predictive Policing ◽  
Trade Offs

Predictive analytics and artificial intelligence are applied widely across law enforcement agencies and the criminal justice system. Despite criticism that such tools reinforce inequality and structural discrimination, proponents insist that they will nonetheless improve the equality and fairness of outcomes by countering humans’ biased or capricious decision-making. How can predictive analytics be understood simultaneously as a source of, and solution to, discrimination and bias in criminal justice and law enforcement? The article provides a framework for understanding the techno-political gambit of predictive policing as a mechanism of police reform—a discourse that I call “predictive policing for reform.” Focusing specifically on geospatial predictive policing systems, I argue that “predictive policing for reform” should be seen as a flawed attempt to rationalize police patrols through an algorithmic remediation of patrol geographies. The attempt is flawed because predictive systems operate on the sociotechnical practices of police patrols, which are themselves contradictory enactments of the state’s power to distribute safety and harm. The ambiguities and contradictions of the patrol are not resolved through algorithmic remediation. Instead, they lead to new indeterminacies, trade-offs, and experimentations based on unfalsifiable claims. I detail these through a discussion of predictive policing firm HunchLab’s use of predictive analytics to rationalize patrols and mitigate bias. Understanding how the “predictive policing for reform” discourse is operationalized as a series of technical fixes that rely on the production of indeterminacies allows for a more nuanced critique of predictive policing.

The Justice Department's Pattern-or-Practice Police Reform Program, 1994–2017: Goals, Achievements, and Issues

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Samuel Walker
Keyword(s):  
Academic Research ◽  
Annual Review ◽  
Publication Date ◽  
Law Enforcement Agencies ◽  
Police Reform ◽  
Social Scientists ◽  
Reform Program ◽  
Or Practice ◽  
Consent Decrees ◽  
The Impact

The Department of Justice's pattern-or-practice police reform program has been an unprecedented event in American policing, intervening in local and state law enforcement agencies as never before and requiring a sweeping package of reforms. The program has reached reform settlements with forty agencies, including twenty with judicially enforced consent decrees. Academic research on the program, however, has been fairly modest. Social scientists have largely focused on a few selected issues. There is no study of the full impact of the program on one agency, and there is no comprehensive study of the impact of the program as a whole. Evaluations of individual agencies have been generally favorable, although with backsliding in some agencies. This review argues that the combination of several major goals and the various elements of specific consent decree reforms have created a web of accountability that is unmatched by any previous police reform effort. Expected final online publication date for the Annual Review of Criminology, Volume 5 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Systematization and improvement legal regulation of the use of artificial intelligence in law enforcement

2020 ◽  
Vol 7 (1) ◽  
pp. 106-112
Author(s):  
Vadim K. Barchukov
Keyword(s):  
Artificial Intelligence ◽  
Law Enforcement ◽  
Russian Federation ◽  
Information Technologies ◽  
Ethical Issues ◽  
Legal Regulation ◽  
Information Support ◽  
Law Enforcement Agencies ◽  
Effective Work ◽  
The Russian Federation

The article systematizes legal acts at the international, Federal and departmental levels on the use of artificial intelligence in law enforcement. In particular, at the international level, the corresponding legal act, according to the author, should contain three components of legal regulation: 1) regulate the organization and construction of an artificial intelligence system between States; 2) determine the principles of functioning of artificial intelligence; 3) regulate the ethical issues of using artificial intelligence. The legal basis for the use of artificial intelligence systems in law enforcement at the Federal level, in addition to the Constitution of the Russian Federation, should be the Strategy for the development of the information society in the Russian Federation (Strategy) for 2017-2030, as well as some special Federal laws (for example, the Law of Moscow), which are designed to specify the mechanism for using the advantages of artificial intelligence in all spheres of public life, including law enforcement. The author notes that the mentioned strategy defines only the General provisions of the state policy on the development of information technologies and artificial intelligence technologies. At the same time, the implementation of national interests outlined in the Strategy is impossible without the effective work of law enforcement agencies, whose functioning, in turn, is impossible without a well-built system of interaction between information support and artificial intelligence. The final part of the paper presents some proposals for improving the legal regulation of the use of artificial intelligence in law enforcement, in particular, justifies the need to adopt a national Doctrine for the use of a Unified system of information support and artificial intelligence in the activities of law enforcement agencies.

scholarly journals Justification of the Necessity to Adjust Law Enforcement Agencies to Digital Transformation of Criminality

Lex Russica ◽  
2021 ◽  
pp. 63-70
Author(s):  
M. A. Zheludkov
Keyword(s):  
Artificial Intelligence ◽  
Law Enforcement ◽  
Public Relations ◽  
Criminal Behavior ◽  
Industrial Revolution ◽  
Digital Technologies ◽  
High Tech ◽  
Social Changes ◽  
Law Enforcement Agencies ◽  
Use Of Technology

Underestimation of the importance of solving problems in law enforcement agencies in the context of the use of new digital technologies by criminals may lead to the situation when high-tech crime does not replace traditional forms of theft, but may result in a sharp hypoxia of preventive activities, which will lack the planned law enforcement resources and means of responding to emerging threats. The relevance of the paper lies in the fact that when assessing the use of modern digital technologies in scientific literature and regulatory support, special emphasis is placed on specific data functions of technologies to automatically analyze a set of data and with the help of a set of algorithms to make decisions on optimization of processes and activities, that is, the ability to simplify human relationships. However, the experience of law enforcement has shown that the lack of law enforcement support for protecting the society from negative use of technology leads to the fact that the response now follows the criminal consequences of their use of technologies. The creation of artificial intelligence (AI) systems has led to serious social changes, there is a kind of transformation of public relations under the influence of the digital economy, which inevitably creates the prerequisites for the emergence of specific criminal behavior. The paper provides for a brief analysis of information from open sources about the possibilities of criminal use of artificial intelligence. The author aims to analyze the various possibilities of creating a new model of protection against cybercrime under the control of law enforcement agencies in the context of new threats caused by criminal use of AI technology and the new industrial revolution.

scholarly journals International trends in the interaction between law enforcement bodies in the digital space

2021 ◽  
Vol 7 (Extra-D) ◽  
pp. 560-565
Author(s):  
Yuri A. Kovtun ◽  
Eduard A. Vinokurov ◽  
Irina Y. Kapustina ◽  
Andrey V. Lagutochkin ◽  
Dmitry V. Stepanov
Keyword(s):  
Artificial Intelligence ◽  
Law Enforcement ◽  
International Law ◽  
Digital Technologies ◽  
Main Task ◽  
Law Enforcement Agencies ◽  
Digital Space ◽  
Blockchain Technology ◽  
International Trends ◽  
Law Enforcement Agency

The authors substantiate the feasibility of introducing digital technologies into modern law enforcement. The position on the need for normative consolidation of the procedure for the creation and use of international databases of law enforcement agencies based on blockchain technology, and the introduction of artificial intelligence technologies is substantiated. The need for a phased, but offensive implementation of digital technologies in law enforcement is determined. The main task for successful international integration is the complete digitalization of law enforcement within each country and on the basis of uniform standards that will allow all individual law enforcement agencies to be combined into a single international law enforcement agency.

Local Government Dependence on Criminal Justice Revenue and Emerging Constraints

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Shannon R. Graham ◽  
Michael D. Makowsky
Keyword(s):  
Criminal Justice ◽  
Law Enforcement ◽  
Local Government ◽  
Criminal Justice System ◽  
Justice System ◽  
The United States ◽  
Yield Potential ◽  
Annual Review ◽  
Publication Date ◽  
Negative Consequences

Revenue generated through the criminal justice system has become a key component of local government budgets across the United States. Although numerous restrictions exist to constrain traditional sources of revenue, only recently have legislators introduced checks on the fiscal profitability of fines, fees, forfeitures, and asset seizures. Left unrestricted, fiscal incentives have demonstrably manifested in the enforcement patterns and discretionary decisions of police. The transformation of officers into agents of revenue creation leads to increased targeting of minority populations and out-of-towners, with emphasis on arrests that yield potential property seizure, with negative consequences for both community trust and the provision of public safety. Those burdened with legal financial obligations are disproportionately poor, positioning the criminal justice system as a pointedly regressive form of taxation. We discuss the mechanisms behind criminal justice revenue generation, the consequences to law enforcement outcomes, and policies designed to reform and mitigate revenue-driven law enforcement. Expected final online publication date for the Annual Review of Criminology, Volume 4 is January 13, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Illicit Bitcoin transactions: challenges in getting to the who, what, when and where

2018 ◽  
Vol 21 (3) ◽  
pp. 297-313 ◽  
Author(s):  
Angela S.M. Irwin ◽  
Adam B. Turner
Keyword(s):  
Machine Learning ◽  
Law Enforcement ◽  
Cyber Security ◽  
Optimal Solution ◽  
Law Enforcement Agencies ◽  
Content Type ◽  
Predictive Policing ◽  
External Data ◽  
Functional Software ◽  
Behaviour Models

Purpose The purpose of this paper is to highlight the intelligence and investigatory challenges experienced by law enforcement agencies in discovering the identity of illicit Bitcoin users and the transactions that they perform. This paper proposes solutions to assist law enforcement agencies in piecing together the disparate and complex technical, behavioural and criminological elements that make up cybercriminal offending. Design/methodology/approach A literature review was conducted to highlight the main law enforcement challenges and discussions and examine current discourse in the areas of anonymity and attribution. The paper also looked at other research and projects that aim to identify illicit transactions involving cryptocurrencies and the darknet. Findings An optimal solution would be one which has a predictive capability and a machine learning architecture which automatically collects and analyses data from the Bitcoin blockchain and other external data sources and applies search criteria matching, indexing and clustering to identify suspicious behaviours. The implementation of a machine learning architecture would help improve results over time and would be less manpower intensive. Cyber investigators would also receive intelligence in a format and language that they understand and it would allow for intelligence-led and predictive policing rather than reactive policing. The optimal solution would be one which allows for intelligence-led, predictive policing and enables and encourages information sharing between multiple stakeholders from the law enforcement, financial intelligence units, cyber security organisations and fintech industry. This would enable the creation of red flags and behaviour models and the provision of up-to-date intelligence on the threat landscape to form a viable intelligence product for law enforcement agencies so that they can more easily get to the who, what, when and where. Originality/value The development of a functional software architecture that, in theory, could be used to detected suspicious illicit transactions on the Bitcoin network.

scholarly journals Epistemologies of predictive policing: Mathematical social science, social physics and machine learning

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172110031
Author(s):  
Jens Hälterlein
Keyword(s):  
Machine Learning ◽  
Law Enforcement ◽  
Social Science ◽  
Justice System ◽  
Black Box ◽  
Law Enforcement Agencies ◽  
Predictive Policing ◽  
Social Physics ◽  
The Status ◽  
Insight Into

Predictive policing has become a new panacea for crime prevention. However, we still know too little about the performance of computational methods in the context of predictive policing. The paper provides a detailed analysis of existing approaches to algorithmic crime forecasting. First, it is explained how predictive policing makes use of predictive models to generate crime forecasts. Afterwards, three epistemologies of predictive policing are distinguished: mathematical social science, social physics and machine learning. Finally, it is shown that these epistemologies have significant implications for the constitution of predictive knowledge in terms of its genesis, scope, intelligibility and accessibility. It is the different ways future crimes are rendered knowledgeable in order to act upon them that reaffirm or reconfigure the status of criminological knowledge within the criminal justice system, direct the attention of law enforcement agencies to particular types of crimes and criminals and blank out others, satisfy the claim for the meaningfulness of predictions or break with it and allow professionals to understand the algorithmic systems they shall rely on or turn them into a black box. By distinguishing epistemologies and analysing their implications, this analysis provides insight into the techno-scientific foundations of predictive policing and enables us to critically engage with the socio-technical practices of algorithmic crime forecasting.

Law enforcement agencies’ participation in the military surplus equipment program

2016 ◽  
Vol 39 (4) ◽  
pp. 791-806 ◽  
Author(s):  
Thomas C. Johnson ◽  
J. Andrew Hansen
Keyword(s):  
Law Enforcement ◽  
Media Coverage ◽  
Program Participation ◽  
Law Enforcement Agencies ◽  
Content Type ◽  
Local Law Enforcement ◽  
Military Vehicles ◽  
The Usa ◽  
State And Local ◽  
The Military

Purpose The police response to the protests in Ferguson, Missouri refocused attention on law enforcement agencies’ participation in the military surplus equipment program, or 1,033 Program. Given the extensive media coverage, particularly regarding the acquisition of military vehicles and weapons, it is important to empirically investigate the 1,033 Program. The paper aims to discuss these issues. Design/methodology/approach Survey data were collected from 1,205 law enforcement regarding 1,033 Program participation and the types of equipment acquired. This study also explores whether significant differences exist by region, agency type, and size. Findings The results demonstrated that while a majority of state and local law enforcement agencies participated in the 1,033 Program, a significant number of agencies did not. Acquisition of military surplus weapons, vehicles, and other types of equipment was not the norm. Significant differences were discerned for overall participation and equipment acquisition based on region, agency type, and size. Research limitations/implications This study does not address other concerns such as how surplus military equipment is used or policies agencies may have to govern that use, which provide avenues for further research to discern best practices. Practical implications Law enforcement agencies do not appear to acquire military vehicles and weapons to the extent that has been dramatized by the media. Originality/value This study contributes empirical data to inform the discussion of law enforcement agencies’ acquisition of military surplus equipment in the USA.

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