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The 2002 film Minority Report regularly appears in tech press articles asking whether it 'predicted the future'. When such publications invoke the film as having 'predicted the future' or 'come true', what social and political claims are being made? How has Minority Report become a discursive tool for imagining, constructing, and criticizing sociotechnical worlds? In this paper, we evaluate the worldbuilding process and real-world trajectories of three technologies 'from' Minority Report, as refracted through the lens of tech journalism: gestural interfaces, targeted advertising, and predictive policing. We argue that science fiction does more than represent technologies; it participates in their social construction. Some technologies imagined in Minority Report operate as 'diegetic prototypes', and the journalistic witnessing public takes them up in complex ways, interpreting, misinterpreting, and remixing the technologies depicted in the film. We further argue that it is not only technologies that move between film and reality in this process, but entire sociotechnical imaginaries. We find that in tech beat interpretations of Minority Report, the interfaces between bodies and technologies reflect a Silicon Valley sociotechnical imaginary of disembodied cyborg subjects and deracialized surveillance that materially and discursively shapes how technologies depicted in the film are developed and received. [ABSTRACT FROM AUTHOR]

ABSTRACT Despite increases in online child sexual exploitation (OCSE) internationally, no study has examined risk factors for re‐offending among females who perpetrate OCSE, resulting in limited knowledge regarding the idiosyncratic needs of this cohort. This study explored factors predictive of further police contact among 116 females known to police for OCSE offenses in Victoria, Australia. Four binary regressions were run for each of Any, Violent, and Non‐Violent re‐offending, with one regression each focusing on characteristics related to offending history, index offending, co‐offending, and victimization. Predicted probabilities from each model were then input as predictors into a final binary regression for each recidivism type. Offending history emerged as the most robust predictor across re‐offending types, even when controlling for other domains. Victimization and index offending also predicted Non‐Violent re‐offending, while co‐offending was not predictive across any analysis. These preliminary findings have implications for the unique management needs of females who perpetrate OCSE. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Existing research on police recruitment is eclectic, with examples of multiple methodologies in multiple police-related settings. These methods often resemble psychological measurement of individual traits yet neglect the potential recruits' social resources or network-based influence. More recent research has utilised social identity and social network theory to understand the route to a police candidate's eventual recruitment, but this is underdeveloped. This literature indicates that further research utilising social identity theory could assist with understanding what was before for police recruits and whether that matters. This study explores the use of random forest machine learning to analyse one partial and two full self-report social identity measurement instruments completed by 886 police recruit applicants. It aimed to explore whether the results of these instruments completed by potential police recruits were predictive of their success in the recruitment process. The results reveal that the combined use of these validated social identity instruments offers a reliable predictive base for successful and unsuccessful applicants, with an overall accuracy rate of 86% across the model's performance metrics. The implications from this study highlight the significance of perceived social identity in the context of police recruitment, emphasising the potential impact of using its measurement to gain improved understanding of candidate selection. Social identity measurement instruments could be incorporated into recruitment processes, allowing police departments to enhance their ability to identify individuals who are more likely to succeed at an earlier stage via machine learning. Practically, this could reduce the need for multiple, expensive recruitment stages. Theoretically, it illustrates that a police recruit's social identity is of importance to whether a candidate is successful or not, presenting police forces with both challenges and opportunities. [ABSTRACT FROM AUTHOR]

While studies have examined the correlates of institutional trust across Asian countries, few studies have analyzed public attitudes toward two major social control institutions, the police and courts, in the Philippines. This study fills this gap in the literature by exploring the connections between social trust, institutional performance, media exposure and consumption, and trust in the police and courts. Using recent survey data from the Asian Barometer Survey, regression models were employed to assess and compare whether these factors are predictive of police and court trustworthiness. Results reveal the importance of social trust and institutional performance in predicting Filipinos' trust in the police and courts. However, media exposure and consumption were largely ineffective in predicting police and court trust. Given the Philippines' unique historical and political context, these findings contribute to existing literature and provide implications for future research and policy. [ABSTRACT FROM AUTHOR]

This research addresses the potential for tackling crime volumes and improving crime analytics through new enhancement strategies. The use of machine learning and deep learning solutions is increasing in crime prediction, as in many other fields. This study aims to strengthen proactive approaches in criminology by evaluating the effectiveness of the stacking-based ensemble learning (S-BEL) model, which aims to enhance overall performance by combining the strengths of various algorithms to improve crime analytics and facilitate crime prevention strategies. The study analyzes six studies leveraging the S-BEL model for crime prediction, along with 28 research articles on crime prediction, seven studies utilizing ensemble learning models, and 56 research articles leveraging the S-BEL model in general prediction studies. The findings of the study highlight that S-BEL stands out as a prominent technique in crime prediction, providing valuable insights for law enforcement. [ABSTRACT FROM AUTHOR]

The Karawang Regency faces a significant increase in the number of crimes, being a major concern due to its disruptive impact on the community as well as serious challenges for law enforcement and local governments. In order to respond to the various problems faced by Bhabinkamtibmas in the field, support and strengthening are needed with the right policing program. The research contributes to the field of public security by introducing a Predictive Policing framework tailored to the needs of Cilempung Village, Karawang, utilizing information technology and data analysis to identify crime patterns and predict potential crimes. The study provides insights into the factors influencing crime rates and offers practical strategies for implementing predictive polcing in local communities, ultimately aiming to enhance public safety and maintain social order. [ABSTRACT FROM AUTHOR]

When Palantir Technologies customized the Gotham platform into POL-INTEL, a data integration and analysis platform purchased and used by the Danish police, it also enforced a new ontology that simultaneously shaped the police organization and policing practices. In this context, the concept of ontology should be understood in a twofold, albeit interconnected, way: it stands for its usual philosophical burden, but also refers to a centralized concept repository. In Computer Science, ontology refers to the basic concepts for how data is structured. Based on a series of interviews with Palantir engineers and police-officer users of POL-INTEL, this paper investigates what kind of politics, concepts, and bias in the form of data are inserted, processed and materialized in and by POL-INTEL's ontology. We argue that POL-INTEL's ontology is inherently political, as it is articulated by an assemblage of data, ideological positions, and economic concerns that are translated into the Danish context. POL-INTEL as a vector of platformization of police work also implies ideological choices with important consequences for the organizational life and accountability of law enforcement, establishing new distributions of skills between the platform and police officers. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article examines the rise of algorithmic policing in the United States, focusing on predictive policing platforms and facial recognition technologies. Part 1 explores how companies like PredPol, Palantir and Clearview AI have developed data‐driven tools to predict and prevent crime and identify suspects. The article analyses the historical context, technological development and implementation of these systems by law enforcement agencies. It also discusses the concerns raised by critics, including issues of privacy, racial bias and the potential for reinforcing discriminatory policing practices. Through case studies and examples, the article illustrates how these technologies have impacted communities, particularly communities of colour. It examines the complex interplay between Silicon Valley startups, venture capital and law enforcement in shaping modern policing practices. [ABSTRACT FROM AUTHOR]

This article examines the role of statistical analysis in criminology, thoroughly investigating its diverse influence and uses. The introduction provides a comprehensive analysis of the convergence of statistics and criminology, highlighting the significance of statistical methodologies in comprehending and mitigating criminal conduct and augmenting the efficacy of the criminal justice system via statistical techniques. The following section examines statistical analysis's impact on comprehending crime trends, facilitating policy development, and constructing effective crime prevention tactics. Real-world case studies show statistical analysis's practical use and beneficial effects in criminological practice. Notwithstanding the many benefits, it is indispensable to acknowledge and examine the possible obstacles and constraints associated with using statistical analysis in criminology. These include but are not limited to difficulties about data quality, ethical considerations, and resource constraints. Considering the abovementioned factors, we propose prospective resolutions and alternative measures to address these obstacles effectively. In anticipation of forthcoming developments, we draw attention to emerging patterns and advancements in the field, including big data analytics, machine learning, and artificial intelligence, which have the potential to augment the capabilities and efficacy of statistical analysis within the realm of criminology. The conclusion effectively integrates the main topics examined, affirming the crucial significance of statistical analysis in the progression of criminological research, policy formulation, and practical implementation. Furthermore, it highlights the contribution of statistical analysis towards establishing safer communities and a fairer criminal justice system. [ABSTRACT FROM AUTHOR]

This article builds a theoretical framework with which to confront the racializing capabilities of artificial intelligence (AI)-powered real-time Event Detection and Alert Creation (EDAC) software when used for protest detection. It is well-known that many AI-powered systems exacerbate social inequalities by racializing certain groups and individuals. We propose the feminist concept of performativity, as defined by Judith Butler and Karen Barad, as a more comprehensive way to expose and contest the harms wrought by EDAC than that of other "de-biasing" mechanisms. Our use of performativity differs from and complements other Social Studies of Science and Technology (STS) work because of its rigorous approach to how iterative, citational, and material practices produce the effect of race. We focus on Geofeedia and Dataminr, two EDAC companies that claim to be able to "predict" and "recognize" the emergence of dangerous protests, and show how their EDAC tools performatively produce the phenomena which they are supposed to observe. Specifically, we argue that this occurs because these companies and their stakeholders dictate the thresholds of (un)intelligibility, (ab)normality, and (un)certainty by which these tools operate and that this process is oriented toward the production of commercially actionable information. [ABSTRACT FROM AUTHOR]

Racial capitalism depends on the reproduction of an existing racialized economic order. In this article, I argue that the disavowal of past injustice is a central way in which this reproduction is ensured and that market-based forms of knowledge production, such as for-profit predictive practices, play a crucial role in facilitating this disavowal. Recent debates about the fairness of algorithms, data justice, and predictive policing have intensified long-standing controversies, both popular and academic, about the way in which statistical and financial modes of accounting and predicting articulate, represent and produce ascriptive categories of hierarchically ordered social difference, and reproduce unjust social hierarchies and inequalities. These debates have productively problematized the racial lives of seemingly apolitical predictive technologies and demanded the re-politicization of predictive practices. What has been missing from these debates so far, however, is a more explicit engagement with ways in which anti-racist movements and activists themselves have contested the entanglements of prediction and race making. I turn to a recent prominent example, namely the contestation over racial discrepancies in subprime lending to examine how fair lending activists have conceptualized and troubled the reproduction of a racialized economic order through for-profit predictive practices in the decade before the Great Financial Crisis. I situate this particular example in the broader historical and political context of politicizing prediction that first emerged with the ascendancy of a liberal, individualist-proprietary conception of risk, and the political problem space to which this has given rise. My analysis shows that actuarial conceptions of fairness continue to reverberate in anti-racist contestations of for-profit predictive practices, and that they tend to marginalize and undercut more radical strands of critique of the racialization of financial markets. Insofar as these modalities of contestation implicitly reproduce a liberal, proprietary-individualist conception of risk, I argue, they fail to effectively challenge the quasi-alchemical transformation of injustice into personal responsibility, and thus contribute to the disavowal of past injustice. [ABSTRACT FROM AUTHOR]

This article examines the political work performed by the weather within contemporary big data predictive analytics. As a sociotechnical construction, the weather has long served as a predictive medium for forecasting human behaviour across historical contexts in order to reinforce settler colonial and imperial projects of spatial domination. Now, within twenty-first century big data systems, the weather has become a proxy for predicting human activity. Examining industrial materials from the predictive policing software HunchLab and the social media sentiment analysis platform Social Doppler, I argue that the weather proxy underwrites claims to prediction by collapsing the distinction between correlation and causation. Key here is how the proxy remediates the mechanism of impressibility that structured the weather as a racial technique for population management within nineteenth century statistical research. Through the weather proxy, big data programmes channel biopolitical frameworks of impressibility – or impressibility by proxy – in ways that causally bind individual behaviour to group-differentiated vulnerabilities of environmental exposure. At stake is how the weather proxy serves as a universal baseline condition for predicting population-level behaviour, which aims to mitigate accusations of racial bias by deferring to environmental conditions beyond human design. As a cultural construction tied to ubiquity, the weather thus is a rhetorical device that works to legitimate predictive analytics as impartial and universal while simultaneously effacing the violence that inheres within algorithmic technologies. Ultimately, by taking a comparative historical approach to the weather, I argue that we might better grasp the transformations in data science that underpin how ubiquity operates as a site of racial violence within contemporary surveillance systems. [ABSTRACT FROM AUTHOR]

The growing digitisation in our society also affects policing, which tends to make use of increasingly refined algorithmic tools based on abstract technologies. But the abstraction of technology, we argue, does not necessarily entail an increase in abstraction of police work. This paper contrasts the 'abstract police' debate with an analysis of police practices that use digital technologies to achieve greater precision. While the notion of abstract police assumes that computerisation distances police officers from their community, our empirical investigation of a geo-analysis unit in a German Land Office of Criminal Investigation shows that the adoption of abstract procedures does not by itself imply a detachment from local reference and community contact. What we call contextual reference can be productively combined with the impersonality and anonymity of algorithmic procedures, leading also to more effective and focused forms of collaboration with local entities. On the basis of our empirical results, we suggest a more nuanced understanding of the digitalisation of police work. Rather than leading to a progressive estrangement from the community of reference, the use of digital techniques can enable experimentation with innovative forms of 'precision policing', particularly in the field of crime prevention. [ABSTRACT FROM AUTHOR]

Batam, located adjacent to Singapore and Malaysia and experiencing rapid population growth in Indonesia, faces the potential for increased security and crime issues. Nolan (2004) notes a correlation between population size and crime volume. As part of the Riau Islands Regional Police area, which falls under the 3T (Disadvantaged, Frontier, Outermost) regions, it is crucial for Batam to implement predictive policing. The aim of this study is to enable the regional police to anticipate security and public order disturbances (kamtibmas) through analysis based on knowledge, data, and appropriate methods, allowing for early prevention. The methodology involves analyzing historical crime data, using predictive algorithms, and employing information technology to identify crime patterns and trends. The findings indicate that implementing predictive policing can significantly reduce crime incidents and enhance law enforcement efficiency. Consistent and precise preventive measures and solutions will enable the police to improve public service quality, strengthen community relations, and foster a positive image as an institution committed to justice, security, and community welfare. [ABSTRACT FROM AUTHOR]

Modern society is characterised by the pervasive presence of information and communication technologies. The growing demands of today’s economy and society for enhanced and efficient products and services have led to the continual advancement of the technological sector. Among these advancements, artificial intelligence stands out as a particularly noteworthy phenomenon. Artificial intelligence entails the capacity of computer programmes to emulate human intelligence and perform a wide array of tasks. Its implementation has ushered in various advantages, allowing individuals to accomplish tasks like online banking, virtual meetings, and digital conversations without the requirement of physical presence. Despite these benefits, the adoption of new technologies also introduces potential risks to fundamental rights and freedoms, including privacy, personal data protection, and individual liberty. The application of artificial intelligence (AI) in the justice system has been a topic of growing interest and debate. AI technologies are being explored and implemented in various aspects of the justice system to improve efficiency, accuracy, and access to justice. [ABSTRACT FROM AUTHOR]

The advent of predictive policing systems demonstrates an increased interest in more novel forms of data processing for the purpose of crime control. This paper draws on interviews with police practitioners in the Netherlands and the UK to deconstruct the rationalities that are embedded within the turn to predictive identification. Debates on predictive policing have predominantly centred data in the analysis of the institutional and societal implication of prediction, linking its use to the premise of efficiency and accuracy and foregrounding issues around bias and discrimination. Yet, little is known about its actual practice. In policing, I find that studying data as practice surfaces new insights into the relationship between risk and the ways in which crime priorities are operationalised and the security mandate of the state is negotiated. Drawing on Harcourt's (2008) observation that the desire to predict crime says more about the police than it does about a potential offender, I argue that predictive identification is not about prediction, nor about efficiency, but rather it is about optimisation. Here, datafication serves to overcome self-defined organisational challenges within the police. [ABSTRACT FROM AUTHOR]

This qualitative study evaluates the effectiveness of predictive policing technologies by examining the implementation within law enforcement operations in Lagos State. The research aims to explore how predictive systems influence crime prevention, and decision-making processes in policing. Drawing on semi-structured interviews with NDLEA officers, the study reveals the perceived benefits and challenges of integrating predictive policing tools into traditional methods. Through thematic analysis of interviews and review of secondary sources, the research identifies key themes such as the tension between technology and human judgement, and the potential for overreliance on system predictions. The findings indicate that while predictive policing can improve efficiency in crime hotspot identification, its efficacy is undermined by concerns over privacy, and fairness. The study concludes that for predictive policing to be effective, it must come with strong regulatory frameworks and ethical guidelines to ensure support from technologies. This research contributes to the growing discourse on the intersection of technology and law enforcement, offering insights into how predictive policing can evolve to better serve the people. [ABSTRACT FROM AUTHOR]

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The VioGén system is a predictive policing and algorithm tool, which has been implemented in Spain since 2007. Its main objective is to assess the risk of a woman of being victim of intimate partner violence and proposing measures for her protection accordingly. This work will focus on one of the most essential parts of the tool’s functioning: the data collection process. I intend to illustrate how this process of data collection can be influenced by the bias and stereotypes that have been constructed within criminal law concerning gender violence victims and the way this affects, in the end, the system’s functioning.

Acoustic gunshot detection systems (AGDS) have been emerging as a technological solution to the growing problem of gun violence around the world. We examine a particularly prominent AGDS technology called RespondTM developed by publicly traded US company ShotSpotter Inc. (NASDAQ: SSTI) to better understand the sociotechnical logics that inform its operation. Drawing from frameworks provided by science and technology studies and sound studies, we ask, "What are the broader conditions that allow for a successful AGDS as it is imagined by ShotSpotter?" At a time in which the accuracy and reliability of ShotSpotter's AGDS are being seriously questioned through the numerous reports of false positives that reached as high as 99 percent in certain cities it was deployed in, it is imperative to interrogate what exactly is "false" in these false positive reports and how the company operates despite them. In this paper, we trace ShotSpotter and its artificial intelligence/machine learning AGDS technologies as they exist across various patents, promotional materials, financial documents, and public statements to not only better understand the ways in which they translate sound into "crime," or space into "crime scene," but also to bring attention to how ShotSpotter translates itself across its different audiences. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Militarized policing strategies aiming to identify and nullify risks to national security in Western nations have become central to the biopolitical regulation of racialized populations. While the disproportionate impact of pre-emptive counter-terrorism policing on 'Muslim' populations has been highlighted, the post-racial techno-politics of predictive policing as a mode of securitization remain overlooked. This article argues that the 'war on terror' is governed by a state of crisis that conditions a pre-emptive biopolitics of containment against (unknown) future threats. We examine how predictive policing is progressively dependent on the computational production of risk to avert impending terror. As such, extant forms of counter-terrorism algorithmic profiling are shown to mobilize post-racial calculative logics that renew racial oppression while appearing race-neutral. These predictive systems and pre-emptive actions, while seeking to securitize the future by identifying and nullifying suspects, evasively remake race as risky, thus rendering security indistinguishable from insecurity. Hence, we assert that state securitization is haunted by a profound sense of racialized dread over terrorism, for it can only resort to containing, rather than resolving, the perceived threat of race. [ABSTRACT FROM AUTHOR]

Artificial intelligence (“AI”) increasingly is used to make important decisions that affect individuals and society. As governments and corporations use AI more pervasively, one of the most troubling trends is that developers so often design it to be a “black box.” Designers create AI models too complex for people to understand or they conceal how AI functions. Policymakers and the public increasingly sound alarms about black box AI. A particularly pressing area of concern has been criminal cases, in which a person’s life, liberty, and public safety can be at stake. In the United States and globally, despite concerns that technology may deepen pre-existing racial disparities and overreliance on incarceration, black box AI has proliferated in areas such as: DNA mixture interpretation; facial recognition; recidivism risk assessments; and predictive policing. Despite constitutional criminal procedure protections, judges have often embraced claims that AI should remain undisclosed in court. Both champions and critics of AI, however, mistakenly assume that we inevitably face a trade-off: black box AI may be incomprehensible, but it performs more accurately. But that is not so. In this Article, we question the basis for this assumption, which has so powerfully affected judges, policymakers, and academics. We describe a mature body of computer science research showing how “glass box” AI—designed to be fully interpretable by people—can be more accurate than the black box alternatives. Indeed, black box AI performs predictably worse in settings like the criminal system. After all, criminal justice data is notoriously error prone, and it may reflect preexisting racial and socioeconomic disparities. Unless AI is interpretable, decisionmakers like lawyers and judges who must use it will not be able to detect those underlying errors, much less understand what the AI recommendation means. Debunking the black box performance myth has implications for constitutional criminal procedure rights and legislative policy. Judges and lawmakers have been reluctant to impair the perceived effectiveness of black box AI by requiring disclosures to the defense. Absent some compelling—or even credible—government interest in keeping AI black box, and given the substantial constitutional rights and public safety interests at stake, we argue that the burden rests on the government to justify any departure from the norm that all lawyers, judges, and jurors can fully understand AI. If AI is to be used at all in settings like the criminal system—and we do not suggest that it necessarily should—the presumption should be in favor of glass box AI, absent strong evidence to the contrary. We conclude by calling for national and local regulation to safeguard, in all criminal cases, the right to glass box AI. [ABSTRACT FROM AUTHOR]

Predictive policing is a tool used increasingly by police departments that may exacerbate entrenched racial/ethnic disparities in the Prison Industrial Complex (PIC). Using a Critical Race Theory framework, we analyzed arrest data from a predictive policing program, the Strategic Subject List (SSL), and questioned how the SSL risk score (i.e., calculated risk for gun violence perpetration or victimization) predicts the arrested individual's race/ethnicity while accounting for local spatial conditions, including poverty and racial composition. Using multinomial logistic regression with community area fixed effects, results indicate that the risk score predicts the race/ethnicity of the arrested person while accounting for spatial context. As such, despite claims of scientific objectivity, we provide empirical evidence that the algorithmically‐derived risk variable is racially biased. We discuss our study in the context of how the SSL reinforces a pseudoscientific justification of the PIC and call for the abolition of these tools broadly. Highlights: Guided by Critical Race Theory, this study assesses the racialization of risk in the SSL The PIC uses surveillance to aid the criminalization of racialized communities Multinomial logistic regression found strong bias in the SSL while controlling for spatial effects Predictive policing tools offer pseudoscientific justification of the PIC and cannot be reformed [ABSTRACT FROM AUTHOR]

Copyright of Public Administration is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traditional notions of crime control often position the police against an individual, known or not yet known, who is responsible for the commission of a crime. However, with increasingly sophisticated technology, policing increasingly prioritizes the prevention of crime, making it necessary to ascertain who, or what class of persons, may be the next likely criminal before a crime can be committed, termed predictive policing. This causes a shift from individualized suspicion toward predictive profiling that may sway the expectations of a police patrol. Classically, where a patrol officer forms reasonable suspicion prior to a stop, it is based upon his/her analysis of the situation taken as a whole in context. However, where a predictive profile is employed, information available to the officer accordingly adjusts his/her perception of context and affects the application of the reasonable suspicion standard. This article addresses the way in which new approaches to forming reasonable suspicion affect the due process protection of individuals' fundamental rights. It argues that while an officer still operates with good faith discretion, using predictive profiling causes reasonable suspicion to be based on an augmented understanding of reality and as a result, due process guarantees are weakened. The rights to non-discrimination and the presumption of innocence are assessed and argued as illustrative of this weakening and shift in policing standards. The article ultimately argues that while predictive policing cannot be categorically labeled as inconsistent with criminal justice, changes as seemingly moderate as the manner in which discretion operates have larger effects on individuals' rights. [ABSTRACT FROM AUTHOR]

Geographical profiling is a form of crime analysis that can be used to help predict the area within which the home or base of a serial offender resides. Police services globally have incorporated it into their crime analysis capabilities but there remains limited research on how this has been achieved. In this article, we interview 10 geographical profilers working in the field to gather their experiences of the operational use of geographical profiling. Implementation theory is used as the theoretical framework to analyze the interviews. As a result, we identify a field with the potential to add value to criminal investigations, but a lack of knowledge and awareness of frontline officers and staff about the novel capability is affecting its use. In addition, issues relating to technology, tasking acceptance criteria, and methods of data collection create compounding factors serving to potentially reduce its contribution. We provide recommendations to address them including greater levels of training, a loosening of theoretical constraints, academic collaboration, and embracing free-to-access technology and heuristic methods as potential ways to improve the application. [ABSTRACT FROM AUTHOR]

By combining securitization literature and literature from the field of border criminologies, this article reflects upon the ongoing securitization of intra-EU East–West mobility. The European Union is built on the inherent tension between the (economic) benefits and risks of one of its core principles: the principle of free movement. The two enlargements of the EU that led to the inclusion of several countries located in Central and Eastern Europe further increased this tension and led countries in Western Europe to officially reconstruct intra-Schengen borders. By looking into recent practices of (border) policing in the Netherlands, this article illustrates how CEE nationals are subjected not only to a securitized discourse around their mobility but also to securitized policing practices that aim to create a division between 'core' and 'non-core' Europeans while at the same time distinguishing between possible 'crimmigrant' others and bona fide travellers. [ABSTRACT FROM AUTHOR]

Objectives: The paper studies the impact of predictive policing on crime in a developing country. It also assesses the impact of different police trainings. Method: We analyze a randomized controlled trial conducted in Montevideo, Uruguay to assess the implementation of a predictive policing software developed in the United States. Half of the precincts were randomly assigned to the software and half to the local crime analysts (status quo). The second experiment allocated randomly a specially trained police force to targeted patrol areas per shift and day. Results: No statistically significant differences were found in crime outcomes between the precincts assigned to the foreign predictive software and those assigned to local crime analysts. On the second experiment, given determined targeted places, the specially trained task force showed more compliance with the assigned patrol sites (20% more patrol time) and a greater potential for reducing crime (reduction of 30% in robberies only during high crime shifts in comparison to the control group (no special training). There is also evidence of a diffusion of benefits to adjacent areas. Conclusions: The implementation of an international predictive policing software did not outperform local crime analysts in terms of crime reduction. Local crime analysts are more cost-effective. Given determined targeted places, a modest increase in police dosage of a specially trained police force could reduce crime in high-crime times. In developing countries new policing technologies and training require a deep understanding of the context to channel limited resources in the most efficient way. [ABSTRACT FROM AUTHOR]