Predictive Policing Research Articles

Automation scenarios: citizen attitudes towards automated decision-making in the public sector

ABSTRACT This article explores citizen attitudes towards automated decision-making (ADM) in the public sector, addressing concerns related to social justice and transparency. ADM, used in diverse public services, such as benefit application processing, welfare fraud detection and tax calculation, has sparked public interest and scepticism. To shed light on this complex issue and make ADM more accessible for citizens, we presented three domain-specific scenarios in a population-representative survey in Estonia (n = 1,500), Germany (n = 2,001) and Sweden (n = 1,000). These scenarios involved job seeker categorisation, child welfare risk assessment and predictive policing through facial recognition. Drawing from this survey and adopting an exploratory approach, we analyse attitudes across responses to these scenarios and conduct a regression analysis, integrating individual variables such as age, gender, education, awareness, enthusiasm and trust in ADM systems. Our findings reveal differences in citizens’ attitudes based on welfare regimes and individual characteristics. This citizen-focused approach underscores the significance of involving citizens in the governance of ADM in the digital welfare state, transcending the traditional regulatory and stakeholder-centric perspectives.

Predictive Policing in the AI Act: meaningful ban or paper tiger?

Predictive Policing in the AI Act: meaningful ban or paper tiger?

The Effectiveness of Big Data-Driven Predictive Policing: Systematic Review

In this study, we aimed to investigate the effectiveness of big data-driven predictive policing, one of the latest forms of technologybased policing, and also the risks of data concentration on police forces or algorithmic bias. In order to properly weigh the benefits and risks, we first conducted a systematic review of the effectiveness of big data-driven predictive policing, based on the terminology of the research topic, and finally extracted 161 articles. We classified them into four categories based on the strength of the evidence and examined the evidence of effectiveness in each category. We concluded that while it is encouraging to see a range of studies, given the significant concerns about big data-driven predictive policing, evidence of effectiveness that can be used by policymakers still needs to be supported by more research, as only 6 out of 161 were evidencestrong in our study categorisation.

Pred-Pol-Pov: Visibility, Data Flows, and the Predictive Policing of Poverty

Predictive and data-driven policing systems continue to proliferate around the world, enticing police forces with promises of improvements in efficiency and the ability to offer various ways of addressing the future to pre-empt, predict, or prevent crime. As more of these systems become operationalised in England and Wales, this paper takes up Duarte’s (2021) observation that there is a lack of description as to what such systems actually are. This paper adapts a social network methodology to explore what is a data-driven policing system. Using a police force in England, UK, as a case study, we provide a visualisation of a data-driven policing system based on the data flows it requires to operate. The paper shows how a disparate network of affiliate organisations act as collators of specific data types that are then used in a range of policing applications. We make visible how data travels from its source through various nodes and the various potential points of translation that occur. We show, as others have argued before us, the data points used are proxies for poverty, making certain groups and sections of society highly visible to the digital system whilst other groups and crimes become less visible—and sometimes even hidden.

Data in Policing: An Integrative Review

ABSTRACT The article brings together contemporary research on how new forms of data are reshaping policing and police organizations. A systematic review of 192 papers (1970–2022) identifies the developing effects of data in areas like automated decision-making and predictive policing. We synthesize the findings in an integrative framework that links data sources, data-driven processes, and policing outcomes while highlighting the significance of institutional, organizational and individual-level moderators. The acceleration of data in policing raises critical questions for public administration scholars. We discuss the implications on the evolving nature of discretion and decision-making, the training of police officers, and tensions between data professionals and street-level officers. We further unpack the implications for organizational transformation and the changing nature of policing strategies.

Leveraging Artificial Intelligence for Crime Detection and Prevention

This paper sheds light on how Artificial Intelligence (AI) is reshaping the landscape of crime detection and prevention, bringing about a significant change in traditional law enforcement methods. With society increasingly embracing AI-driven solutions, it's crucial to understand how they contribute to public safety. We'll dive into various aspects of AI, such as predictive policing, video surveillance analysis, forensic science, and criminal profiling, to see how they enhance law enforcement capabilities and streamline crime resolution processes.Starting with predictive policing, AI algorithms sift through massive datasets to identify patterns and predict future criminal activity, empowering law enforcement to take proactive measures. Similarly, AI-powered video surveillance systems enhance real-time monitoring and anomaly detection, allowing for quick identification of suspicious behavior in urban environments.Moving on, we'll explore AI's impact on forensic analysis, where it expedites processes like DNA sequencing, fingerprint recognition, and ballistics analysis, speeding up the resolution of cold cases. Additionally, we'll delve into AI's role in criminal profiling, highlighting its ability to decipher behavioral patterns and predict criminal motivations, though ethical considerations remain paramount.We'll also address ethical concerns surrounding AI deployment in law enforcement, including privacy issues and algorithmic biases. Emphasizing the need for transparency and accountability, we advocate for responsible use of AI-powered crime detection systems to uphold ethical standards and protect individual rights.

The Effect of Big Data Analytics on Predictive Policing: The Mediation Role of Crisis Management

Objective: The objective of this study is to evaluate the impact of big data analytics (BDA) on predictive policing, particularly examining the mediating role of crisis management in this relationship. Theoretical Framework: The research is anchored in the domain of big data analytics, focusing on its application within law enforcement for enhancing predictive policing capabilities. The study explores how crisis management serves as a linkage between data analytics and predictive policing practices. Method: The study gathered data from 450 individuals working across various police departments in Dubai, utilizing a questionnaire to collect responses. The analytical approach was based on Structural Equation Modeling, conducted using AMOS software. Results and Discussion: Findings from the research indicate that big data analytics significantly boosts predictive policing and crisis management. Importantly, crisis management was identified as a mediating factor between big data analytics and its efficacy in predictive policing. These results suggest that big data analytics not only directly enhances predictive policing but also improves it indirectly through effective crisis management. Research Implications: This study underscores the importance of integrating big data analytics into police operations to advance predictive policing capabilities. It highlights the dual benefits of big data analytics in both direct application and enhancement through crisis management processes. Originality/Value: This research contributes to the limited but growing body of literature on the application of big data analytics in predictive policing. It offers practical guidelines for police forces, especially within the UAE, to better harness big data for improving their operational effectiveness and crisis management strategies. The study also discusses broader implications for both practice and ongoing research in this evolving field.

Applicability Predictive Policing in Indonesia Police Agency

Applicability Predictive Policing in Indonesia Police Agency

Predictive Policing: A Fairness-aware Approach

As Artificial Intelligence (AI) systems become increasingly embedded in our daily lives, it is of utmost importance to ensure that they are both fair and reliable. Regrettably, this is not always the case for predictive policing systems, as evidence shows biases based on age, race, and sex, leading to wrongful identifications of individuals as potential criminals. Given the existing criticism of the system’s unjust treatment of minority groups, it becomes essential to address and mitigate this concerning trend. This study delved into the infusion of domain knowledge in the predictive policing system, aiming to minimize prevailing fairness issues. The experimental results indicate a considerable increase in fairness across all metrics for all protected classes, thus fostering greater trust in the predictive policing system by reducing the unfair treatment of individuals.

Advances in Explainable, Fair, and Trustworthy AI

This special issue encapsulates the multifaceted landscape of contemporary challenges and innovations in Artificial Intelligence (AI) and Machine Learning (ML), with a particular focus on issues related to explainability, fairness, and trustworthiness. The exploration begins with the computational intricacies of understanding and explaining the behavior of binary neurons within neural networks. Simultaneously, ethical dimensions in AI are scrutinized, emphasizing the nuanced considerations required in defining autonomous ethical agents. The pursuit of fairness is exemplified through frameworks and methodologies in machine learning, addressing biases and promoting trust, particularly in predictive policing systems. Human-agent interaction dynamics are elucidated, revealing the nuanced relationship between task allocation, performance, and user satisfaction. The imperative of interpretability in complex predictive models is highlighted, emphasizing a query-driven methodology. Lastly, in the context of trauma triage, the study underscores the delicate trade-off between model accuracy and practitioner-friendly interpretability, introducing innovative strategies to address biases and trust-related metrics.

Citizens’ perspectives on platformisation of police work: a scenario and story-based exploration in Estonia and Sweden

ABSTRACT The integration of automated decision-making systems has transformed police work and our understanding of security and surveillance. Despite a growing theoretical literature on shifts in policing due to widespread analytical platform adoption, the public's understanding and perception of these changes are largely unexplored. This study aims to bridge this gap by empirically examining citizens’ perspectives on the new dynamics of police work in two societies with varying levels of experience with automation in the public sector: Estonia and Sweden. By combining data from a representative, scenario-based quantitative survey conducted among the general population (n = 2500) and qualitative storytelling techniques implemented in classroom settings with students (n = 23) who take classes with a focus on critical data studies, this research seeks to investigate people’s imaginaries, concerns, and expectations regarding predictive policing. The findings shed light on the observation that, in the era of data, the police are not solely perceived as an institution ensuring security or as a source of citizen apprehension related to surveillance. Rather, the transformations in police work are understood as ‘distant technologies’, wherein individuals, be they, citizens, or police officers, are increasingly removed from the direct application of these technologies. This article uncovers that when citizens possess low levels of trust in the police, the implementation of automation can further exacerbate the disconnect between citizens and the state. Furthermore, this research proposes an innovative approach to studying automated systems by combining scenario-based and storytelling methods, thereby making a valuable contribution to methodologies employed in the study of data.

A digitalizáció egyes kihívásai a büntetőeljárásban

Aim: Recent technological developments and their potential consequences pose novel challenges to criminal justice. Digital transformation is a necessary consideration for a modern and effective justice system; however, the rise of information technologies must also be reconciled with procedural safeguards that ensure the right to a fair trial. The aim of this study is to shed light on certain aspects of how digital technologies might affect our interpretation of some of the basic principles of criminal procedure, with an emphasis on the taking of evidence, where such fundamental requirements as the principle of immediacy have perhaps recently been somewhat neglected in favour of conducting the procedure within a reasonable timeframe.Methodology: Our legal framework and justice system must adapt to the advent of the digital age. Certain phenomena, such as Big Data analysis, social media, data and privacy protection, cryptocurrencies, as well as artificial intelligence have recently attracted special attention from legal scholars from all fields of law. The findings of this study are based on the review of the relevant literature in the field of digital technologies and law, as well as legislation and case law from the last few decades and potential future developments.Findings: The expansion of digital technologies into nearly all aspects of our lives is ever-present in our global collective awareness. The digital revolution challenges our previous concepts about communication, access to information and data protection, and has also already changed the way we think about work, education, or commerce. Technology has a massive potential for criminal justiceas well, including the right to a fair trial within a reasonable time. However, it may also contribute further to recent tendencies in the taking of evidence where procedural safeguards such as the principle of immediacy are sacrificed in favour of efficiency.Value: Digital technologies open a vast array of possibilities for criminal justice, however, also allow for new forms of criminality to emerge. Digitalisation and criminal procedure have numerous implications beyond the scope of this study (e.g. predictive policing) that merit further research.

The Role of Predictive Policing in Predicting Spetio-temporal Crime Mapping

Since the modern criminology critically reviews the contribution of socio-environmental factors to the crime mapping in terms of their spatio-temporal distribution, a statistical/machine learning technique namely called the Risk Terrain Modelling-RTM is currently being used as a software solution to diagnose the socio-environmental conditions that lead to crime in a specific area's geography (Study Area), through geospatial and temporal analyses of crimes by linking them to hotspots and analysing them into Big Data (Criminal Data), and as a result, appears the new predicting patterns of risks in geographical area under survey (Survey Area) in future, all that for a sake of a rapid and effective response by the Predictive Police, firstly, for setting the priorities for the deployment of precautionary resources to prevent crime and reduce potential risks in the event of a crime, and secondly, what is required to be done upon arrival as a precautionary step to control the crime with a minimum disadvantage to the police forces. The Risk Terrain Modelling-RTM represents a systematic investment for more than a decade in the field of criminal investigation, it is have been founded by Leslie W. Kennedy and Joel M. Caplan at Rutgers University, and it currently testes in over than 45 countries around the world.

From Bytes to Bars: The Transformative Influence of Artificial Intelligence on Criminal Justice

This research article delves into the impactful integration of artificial intelligence (AI) within the criminal justice system, exploring its transformative implications on crime detection, prevention, and adjudication. Examining applications such as predictive policing, automated legal analysis, facial recognition, and sentencing algorithms, the study highlights the potential benefits, including increased efficiency and accuracy. However, ethical concerns surrounding bias, transparency, and privacy necessitate careful consideration. The article underscores the need for a balanced approach to harnessing AI's potential while addressing these ethical challenges. As AI continues to evolve, collaborative efforts among policymakers, legal professionals, and technologists are imperative to ensure responsible implementation, fostering a criminal justice system that is both technologically advanced and ethically sound. The qualitative research methodology has been applied in the following article.

Machine Learning and the Reproduction of Inequality

Machine learning is the process behind increasingly pervasive and often proprietary tools like ChatGPT, facial recognition, and predictive policing programs. But these artificial intelligence programs are only as good as their training data. When the data smuggle in a host of racial, gender, and other inequalities, biased outputs become the norm.

Crime mapping and hot spot analysis using geospatial data in Jalingo metropolis of Taraba State, Nigeria

Crime mapping and hot spot analysis are topical and significant research across the fields of criminology, data digging, city planning and law enforcement for crime control. This study seeks to identify high crime areas which require an officer presence and predicting the possible response demand to increase the efficiency of response officer patrols. Crime type and patterns exist on a spatial level; these patterns and type can be grouped geographically by physical location, and analyzed contextually based on the region in which crime occurs. The study aimed at analyzing crime hotspots using geospatial data in Jalingo metropolis by identifying the different kinds of crime in the study area, determine the spatio-temporal variation of crime occurrences in the study area and determine the high crime density area (hotspot).This study proposed a method to identifying level crime, localize crime hotspots, identify relationship between spatiotemporal crime patterns and social trends, and analyze the resulting data for the purposes of knowledge discovery and anomaly detection. Several types of crime were analyzed in this dataset, including burglary, bribery, forgery ,murder ,rape ,theft drug abuse, assault, and robbery analysis, several interesting findings were drawn about crime in Jalingo metropolis, including: Crime mapping and hotspots with steadily increasing crime levels, hotspots with unstable crime levels, synchronous changes in crime trends throughout Jalingo metropolis as a whole, individual months in which certain hotspots behaved anomalously, and a strong relationship between crime hotspot locations. This type of statistical and correlative analysis of crime patterns will help law enforcement agencies predict criminal activity, allocate resources, and promote community awareness to reduce overall crime rates in Jalingo metropolis. Policing approaches like patrolling and response to crime incidents can be more effective if proper crime mapping and crime hot spot analysis data are available for policing is used to make decisions on crime control in the study area. Predictive policing in crime management use information such as historical crime data to predict crime patterns and response demand. Historical crime data is used to identify high crime areas through kernel density estimation. It is also used to anticipate the levels of response demand. Both of these factors are used to determine how to direct police patrols. This study revealed how kernel density estimation is used for crime mapping and hot spot.

Institutional factors driving citizen perceptions of AI in government: Evidence from a survey experiment on policing

AbstractLaw enforcement agencies are increasingly adopting artificial intelligence (AI)‐powered tools. While prior work emphasizes the technological features driving public opinion, we investigate how public trust and support for AI in government vary with the institutional context. We administer a pre‐registered survey experiment to 4200 respondents about AI use cases in policing to measure responsiveness to three key institutional factors: bureaucratic proximity (i.e., local sheriff versus national Federal Bureau of Investigation), algorithmic targets (i.e., public targets via predictive policing versus detecting officer misconduct through automated case review), and agency capacity (i.e., necessary resources and expertise). We find that the public clearly prefers local over national law enforcement use of AI, while reactions to different algorithmic targets are more limited and politicized. However, we find no responsiveness to agency capacity or lack thereof. The findings suggest the need for greater scholarly, practitioner, and public attention to organizational, not only technical, prerequisites for successful government implementation of AI.

Organizations and Values in Science and Technology

Abstract This paper articulates a conceptual framework for examining philosophical issues such as the role of values in science at an organizational level. It distinguishes between three dimensions of organizations – organizational aims, organizational structure, and organizational culture – and it examines how these dimensions relate to values in research and development, with a focus on machine learning systems for predictive policing. This framework can be fruitful in identifying interesting and understudied philosophical problems – including those involving inter-organizational divisions of labor – that might otherwise be difficult to conceptualize.

Technology and Civic Virtue

Today, a major technological trend is the increasing focus on the person: technical systems personalize, customize, and tailor to the person in both beneficial and troubling ways. This trend has moved beyond the realm of commerce and has become a matter of public governance, where systems for citizen risk scoring, predictive policing, and social credit scores proliferate. What these systems have in common is that they may target the person and her ethical and political dispositions, her virtues. Virtue ethics is the most appropriate approach for evaluating the impacts of these new systems, which has translated in a revival of talk about virtue in technology ethics. Yet, the focus on individual dispositions has rightly been criticized for lacking a concern with the political collective and institutional structures. This paper advocates a new direction of research into civic virtue, which is situated in between personal dispositions and structures of governance. First, it surveys the discourse on virtue ethics of technology, emphasizing its neglect of the political dimension of impacts of emerging technologies. Second, it presents a pluralist conception of civic virtue that enables us to scrutinize the impact of technology on civic virtue on three different levels of reciprocal reputation building, the cultivation of internal goods, and excellence in the public sphere. Third, it illustrates the benefits of this conceptions by discussing some paradigmatic examples of emerging technologies that aim to cultivate civic virtue.

Application of the Third Generation Crime Prevention Through Environmental Design on University Campuses

As a complex of material and spiritual, space and society, the university campus is a relatively complex environment. This paper investigated the effectiveness of the third generation of Crime Prevention Through Environmental Design (CPTED) in reducing crime and enhancing security within educational institutions. The third generation of CPTED emphasizes the integration of advanced technologies, such as access control systems, intelligent sensors and alarms, emergency communication systems, data analysis, and predictive policing, using advanced technologies to enhance security measures on university campuses. This paper aimed to explore the current security level of the university campus, interpret the principle of the third generation CPTED to reduce the crime rate on college campuses and propose a security strategy based on the third generation CPTED for the university campus. The research method of this paper was qualitative, conducting semi-structured interviews with campus security personnel, university administrators, university teachers, and staff of the Universiti Sains Malaysia, using NVivo software, and using thematic analysis methods for data analysis. The findings suggest that traditional campus security measures may not fully address the evolving security challenges facing modern universities. Participants expressed concern about inadequate lighting, limited surveillance coverage, and inadequate emergency response systems. The results of this paper emphasize the technical application of the third generation CPTED, thereby highlighting the practice of the third generation CPTED in university campus security, proposing the basic strategy of a smart campus, and establishing a mobile phone application on the terminal. Strong use of technology to facilitate real-time communications, incident reporting, and emergency response to enhance campus security and ultimately improve the overall security of the university campus.