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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]

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]

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]

The rise of crime after the Covid-19 pandemic in Jakarta, Jakarta's position as the capital of Indonesia and the transformation of precision police spawned the concept of Polisi RW adapted from community policing, hotspot policing and predictive policing. Polisi RW are expected to be able to conduct crime prevention efforts in the Metro Jaya Regional Police Work Area. The purpose of this study is to test the effectiveness of Polisi RW in reducing the number of crimes and accelerating crime reporting services handled at Metro Jaya Police Department. A quasi-experimental one group pre-post test was selected to test the application of Polisi RW in crime prevention with indicators of reduced crime. The data used in this study is data on the number of crimes reports in the January-February 2023 period as pre and as post period March-April 2023. In addition to the difference between the pre and post ranges, researchers also took measurements in the period March-April 2022 with March-April 2023 as comparison data. Paired sample t-test is used to determine the difference and Cohen's effect size to determine the magnitude of the effect of Polisi RW intervention. Test results on differences in the number of crime reports for the period before and after the implementation of Polisi RW p-value = 0.046, Cohen's effect size of 0.451; The number of crime reports for the period March-April 2022 with March-April 2023 was found to be p=value 0.003, Cohen's d= 1.158. There is an influence of Polisi RW on crime prevention and acceleration of the process of solving crime reports in the Metro Jaya police department. Re-measurement is required after 6 months of application. [ABSTRACT FROM AUTHOR]

Although the practical benefits of forensic DNA phenotyping (FDP) in criminal investigations remain unclear, this emerging forensic technology is the subject of increasing interest from various European stakeholders. However, the risks that the technology poses to data privacy, as well as its negative impact on racialized sub-populations are well documented. Yet, we argue that the abstract nature of the current legal framework and judicial review allows predictive technologies with uncertain outcomes, such as FDP, to still be considered socially desirable while normalizing their adverse consequences on human rights and social equity. We also argue that criminal law and criminal sciences are in a synergistic relationship in which the reification of socially constructed categories of deviancy by forensic technologies contributes to the legitimization of the repressive apparatus, which in turn calls for the use of more investigative tools despite their inherent pitfalls or limits. We conclude with the need to be mindful of this phenomenon when analysing the social costs attached to emerging forensic technologies, as well as to request disclosure from public authorities of comprehensive data regarding its concrete benefits. [ABSTRACT FROM AUTHOR]

Emotional AI is an emerging technology used to make probabilistic predictions about the emotional states of people using data sources, such as facial (micro)-movements, body language, vocal tone or the choice of words. The performance of such systems is heavily debated and so are the underlying scientific methods that serve as the basis for many such technologies. In this article I will engage with this new technology, and with the debates and literature that surround it. Working at the intersection of criminology, policing, surveillance and the study of emotional AI this paper explores and offers a framework of understanding the various issues that these technologies present particularly to liberal democracies. I argue that these technologies should not be deployed within public spaces because there is only a very weak evidence-base as to their effectiveness in a policing and security context, and even more importantly represent a major intrusion to people's private lives and also represent a worrying extension of policing power because of the possibility that intentions and attitudes may be inferred. Further to this, the danger in the use of such invasive surveillance for the purpose of policing and crime prevention in urban spaces is that it potentially leads to a highly regulated and control-oriented society. I argue that emotion recognition has severe impacts on the right to the city by not only undertaking surveillance of existing situations but also making inferences and probabilistic predictions about future events as well as emotions and intentions. [ABSTRACT FROM AUTHOR]

As Gordon writes, 'While police executives designed the redistricting to respond to the exigencies of segregated and unequal neighborhoods, they also sought to I craft i neighborhoods in response to evolving visions of the city' (7). In the post-George Floyd era, few topics are as pressing to urban studies as the role of police in the city. In Chapter 5, Gordon shows how, by regulating where people are seen to be out-of-place in urban space, the police further regulate the racial boundaries between River City's neighborhoods. [Extracted from the article]

Predictive policing has captured the imagination of both enthusiasts hoping to improve public safety and opponents raising concerns around algorithmic bias and opacity. Based on seven in-depth interviews with officers in a UK police force, this article examines the dynamics of how automated risk scores institutionalise an individual-focussed threat-harm-risk strategy aimed at preventing repeat offending. Born out of the need to prioritise work given budget cuts, the risk scores alleviate fears of missing opportunities for prevention and render professional decision-making defendable. Rather than replacing professional judgement, the article finds that officers maintain discretion in a process of co-construction by scrutinising the risk scores and weighing them against other priorities and operational constraints. In a climate of austerity, a concern arises from the scores' potential to drive short-term selective incapacitation rather than prevention through supportive measures. [ABSTRACT FROM AUTHOR]

This study examines police officers' decision-making practices through analyzing how they determine which offenders are candidates for an 810, or peace bond. This legal tool allows police officers to petition the courts for continued surveillance and conditions for offenders postrelease. Little, however, is offered in terms of assessment guidelines on how to make such determinations. As a result, police officers discretionary behaviors and additional legal factors play a key role in these determinations. Our findings advance the idea that "uncertainty" is the central object to be managed, and further complicate how risk is constructed and mobilized by suggesting that risk assessments result in over-precautionary practices. [ABSTRACT FROM AUTHOR]

This paper analyzes how in predictive policing, data 'make' criminal futures that can be subjected to crime prevention. In doing so, it shows that the production of specific accounts of the future is predicated on the data practices of police departments. Specifically, the analysis identifies two sets of data practices that play a key role in how predictive policing brings specific criminal futures into being and renders them amenable to crime prevention measures. The first one pertains to the production of crime data and draws attention to the digital devices and interfaces that co-constitute how criminal phenomena are recorded and classified. The second one relates to 'quality control', i.e. the corrections and updates that keep on transforming crime data even after their original production. Theoretically, the analysis builds on Law and Urry's concept of 'enactment' and substantiates how data-driven analytics perform fluid versions of social realities and simultaneously subject them to interventions. The conceptual lens of enactment allows us to understand how seemingly mundane activities such as producing reports from crime scenes and amending case files throughout an investigation are performative in the sense that they contribute to the making of crime forecasts and corresponding prevention measures. The criminal futures that the police target in an effort to render crime prevention more effective and efficient should thus be understood as contingent on the everyday practices of field officers and analysts. [ABSTRACT FROM AUTHOR]

We develop and calibrate stochastic continuous models that capture crime dynamics in the city of Valencia, Spain. From the emergency phone, data corresponding to three crime events, aggressions, stealing and women alarms, are available from the year 2010 until 2020. As the resulting time series, with monthly counts, are highly noisy, we decompose them into trend and seasonality parts. The former is modeled by geometric Brownian motions, both uncorrelated and correlated, and the latter is accommodated by randomly perturbed sine-cosine waves. Albeit simple, the models exhibit high ability to simulate the real data and show promising for crimes-interaction identification and short-term predictive policing. [ABSTRACT FROM AUTHOR]

Spatial crime risk assessment is based on the identification of the environmental conditions that could facilitate crime. Previous applications of this approach mainly rely on single-level analyses neglecting that different contextual factors are likely to influence crime at different geographical levels and to interact with one another in defining crime risk. This study proposes to innovate this approach by using a multi-level analysis and estimating the interaction terms among environmental features and neighborhood characteristics. An empirical test is conducted in a large urban area. The results prove that this method significantly increases the predictive capacity and favors more complete interpretations of the underlying criminogenic mechanisms, thus orienting more effective consequential actions. [ABSTRACT FROM AUTHOR]

Early detection of people's suspicious behaviors can aid in the prevention of crimes and make the community safer. Existing methods that are focused on identifying abnormal behaviors from video surveillance that are based on computer vision, which are more suitable for detecting ongoing behaviors. While criminals intend to avoid abnormal behaviors under surveillance, their suspicious behaviors prior to crimes will be unconsciously reflected in the trajectories. Herein, we characterize several suspicious behaviors from unusual movement patterns, unusual behaviors, and unusual gatherings of people, and analyze their features that are hidden in the trajectory data. Meanwhile, the algorithms for suspicious behavior detection are proposed based on the main features of the corresponding behavior, which employ spatiotemporal clustering, semantic annotation, outlier detection, and other methods. A practical trajectory dataset (i.e., TucityLife) containing more than 1000 suspicious behaviors was collected, and experiments were conducted to verify the effectiveness of the proposed method. The results indicate that the proposed method for suspicious behavior detection has a recall of 93.5% and a precision of 87.6%, demonstrating its excellent performance in identifying the possible offenders and potential target places. The proposed methods are valuable for preventing city crime and supporting the appropriate allocation of police resources. [ABSTRACT FROM AUTHOR]

In Chapter 2, I Control, i Shapiro follows gig workers: the human bodies commissioned to enact smart logics of maximum flexibility and modularity, to ensure that goods and money flow with as little "friction" as possible. Drawing from his own time working as a delivery worker for Caviar in Philadelphia, Shapiro vividly demonstrates the lack of predictability and control for the gig worker. [Extracted from the article]

ShotSpotter technology is a rapid identification and response system used in ninety American cities that is designed to detect gunshots and dispatch police. ShotSpotter is one of many powerful surveillance tools used by local police departments to purportedly help fight crime, but they often do so at the expense of infringing upon privacy rights and civil liberties. This Article expands the conversation about ShotSpotter technology considerably by examining the adjacent Fourth Amendment issues emanating from its use. For example, law enforcement increasingly relies on ShotSpotter to create reasonable suspicion where it does not exist. In practice, the use of ShotSpotter increases the frequency of police interactions, which also increases the risk of Black Americans becoming the victims of police brutality or harassment. Such racialized policing facilitates the status quo of violence and bias against Black Americans. This Article uses recent cases from the D.C., the Fourth, and Seventh Circuits as a foundation to argue that officers arriving on the scene to investigate a gunshot sound they were alerted of via ShotSpotter technology should not be allowed to use the gunshot sound as the basis of reasonable suspicion and subsequent search and seizure. At the heart of this Article is the argument that the use of ShotSpotter technology is unconstitutional under City of Indianapolis v. Edmond because it is not used for a specific law enforcement purpose beyond preventing crime generally. Under the reasoning and result of Edmond, law enforcement is prohibited from using ShotSpotters unless officers have reasons for individualized suspicion. Spending more money on ineffective ShotSpotters placed in "high crime" neighborhoods across America is not the answer to reducing gun violence. As seen with Oakland's successful Ceasefire program, there are innovative ways to simultaneously build trust in communities and curb gun violence. Indeed, properly designed group violence reduction strategies will foster and maintain dignity for participants in a program tailored to saves lives and promote community healing. [ABSTRACT FROM AUTHOR]

The internet of things (IoT) and the smart city crusade has seen an exponential increase in the number of data points we collect. Cities can use this data for an exceedingly heterogeneous range of purposes such as traffic, parking and public safety. Quite evidently, public safety is an essential pillar of these interconnected, instrumented and intelligent cities. The big data from cyber‐physical systems and social media platforms can be used for predictive policing to identify potential criminal activities, abuse, offenders, and victims of abuse. This study offers a systematic literature review on the use of soft computing techniques for abuse detection in the complex cyber–physical–social big data systems in cognitive smart cities. The objective is to define and identify the diverse concept of abuse and systematize techniques for automatic abuse detection for cyber abuse detection on social media and real‐time abuse detection using IoT. The cyber abuse studies on social media platforms have further been categorized as cyber‐hate and cyberbullying whereas the real‐time abuse includes studies using Internet of cyber‐physical systems. As in a cognitive smart city, citizens expect more from their urban environments with minimal intervention, this study helps to establish the need to capture situational context and awareness in real‐time and foster the need to develop a proactive as well as reactive safety mechanism to mitigate the risks of online abuse. The need of the hour is to entrench self‐learning, thinking and understanding capabilities into the physical and social world for reinforcing intelligent mechanisms which can detect assaults and disorderly conduct. [ABSTRACT FROM AUTHOR]

Policing in many parts of the world—the United States in particular—has embraced an archetypal model: a conception of the police based on the tenets of individuated archetypes, such as the heroic police "warrior" or "guardian." Such policing has in part motivated moves to (1) a reallocative model: reallocating societal resources such that the police are no longer needed in society (defunding and abolishing) because reform strategies cannot fix the way societal problems become manifest in (archetypal) policing; and (2) an algorithmic model: subsuming policing into technocratic judgements encoded in algorithms through strategies such as predictive policing (mitigating archetypal bias). This paper begins by considering the normative basis of the relationship between political community and policing. It then examines the justification of reallocative and algorithmic models in light of the relationship between political community and police. Given commitments to the depth and distribution of security—and proscriptions against dehumanizing strategies—the paper concludes that a nonideal-theory priority rule promoting respect for personhood (manifest in community and dignity-promoting policing strategies) is a necessary condition for the justification of the above models. [ABSTRACT FROM AUTHOR]

Aside from the fact that David Klinger may reasonably point to his numerous training and consultancy engagements with police agencies, such objection may reasonably be countered on the basis that - numbering as they do among "the policed" - these citizens have as much authority as the police themselves in determining what ethical policing looks and feels like. It is his "cynical supposition" that 'keeping police injury data sloppy and obscure serves the interest of police and police departments by protecting the currently loose and permissive standards of police use of deadly force from any plausible scrutiny' (p. 117). Sociologist Michael Sierra-Arevalo brings Part 1 to a close with analysis of and insights into the activities that police agencies value most, noting that both police and public conceptions of "real police work" tend to define policing as "crime-fighting", "dangerous", and "masculine" (p. 68): popular perceptions each of which can be argued to be a mis-conception. In various jurisdictions, including the UK (through the work of the National Police Ethics Committee and the Regional Police Ethics Networks, for instance), police practitioners are beginning to talk about ethics and what it means to police ethically. [Extracted from the article]

This article examines the impact of new AI‐related technologies in data mining and big data on important research questions in crime analytics. Because the field is so broad, the review focuses on a selection of the most important topics. Challenges for information management, and in turn law and society, include: AI‐powered predictive policing; big data for legal and adversarial decisions; bias using big data and analytics in profiling and predicting criminality; forecasting crime risk and crime rates; and, regulating AI systems. This article is categorized under:Algorithmic Development > Spatial and Temporal Data MiningFundamental Concepts of Data and Knowledge > Big Data MiningTechnologies > Artificial IntelligenceApplication Areas > Data Mining Software Tools [ABSTRACT FROM AUTHOR]

Law-enforcement agencies are increasingly able to leverage crime statistics to make risk predictions for particular individuals, employing a form of inference that some condemn as violating the right to be "treated as an individual." I suggest that the right encodes agents' entitlement to a fair distribution of the burdens and benefits of the rule of law. Rather than precluding statistical prediction, it requires that citizens be able to anticipate which variables will be used as predictors and act intentionally to avoid them. Furthermore, it condemns reliance on various indexes of distributive injustice, or unchosen properties, as evidence of law-breaking. [ABSTRACT FROM AUTHOR]

Objectives: Building on Hägerstrand's time geography, we expect temporal consistency in individual offending behavior. We hypothesize that repeat offenders commit offenses at similar times of day and week. In addition, we expect stronger temporal consistency for crimes of the same type and for crimes committed within a shorter time span. Method: We use police-recorded crime data on 28,274 repeat offenders who committed 152,180 offenses between 1996 and 2009 in the greater The Hague area in the Netherlands. We use a Monte Carlo permutation procedure to compare the overall level of temporal consistency observed in the data to the temporal consistency that is to be expected given the overall temporal distribution of crime. Results: Repeat offenders show strong temporal consistency: they commit their crimes at more similar hours of day and week than expected. Moreover, the observed temporal consistency patterns are indeed stronger for offenses of the same type of crime and when less time has elapsed between the offenses, especially for offenses committed within a month after the prior offense. Discussion: The results are consistent with offenders having recurring rhythms that shape their temporal crime pattern. These findings might prove valuable for improving predictive policing methods and crime linkage analysis as well as interventions to reduce recidivism. [ABSTRACT FROM AUTHOR]

In the wider spectrum of Taiwanese public service spheres, the herculean services and dedication of its committed Police personnel have long been recognized, respected, and admired. However, regrettably, question marks concerning their conduct, discipline, and abuse of power have surfaced on intermittent occasions. A classic example that lingers in the public memory is the bribing of Taiwanese video game companies to some unscrupulous elements of the police department, in the closing decades of the 20th century that triggered public outrage and called for scrutiny concerning serious lapses in the discipline and conduct of Police personnel. This research paper endeavors to understand, analyze and address some of those issues based on empirical data on the police personnel of certain specific work zones/areas taking into account holistically both the sentenced police officers vis-à-vis the law-abiding police officers. This module looks into and sieves through available data for seven critical variables, including their degree of variation through the Identification and Analysis Method to develop a Predictive Model on Police Ethics and the important factors that affect Police Ethics. Concretely based on the integrated research, it is proposed that this Predictive Model has good applicability as well as accurate predictive ability in addressing the core issues that affect Police Ethics. It is hoped that through this Early Warning Predictive Model—all the stakeholders that are Policy and Decision-makers, Regulatory Police Agencies but more importantly the Police personnel themselves would effectively address the criticality of the issues that affect the Police Ethics so as to undertake competent and effective measures to erase/lessen the menace and provide an early rehabilitative care/assistance to build a strong, constructive and visionary Taiwanese Police Force to meet the challenges of 21st century and beyond. [ABSTRACT FROM AUTHOR]

Police agencies expend considerable effort to anticipate future incidences of criminal behaviour. Since a large proportion of crimes are committed by a small group of individuals, preventive measures are often targeted on prolific offenders. There is a long-standing expectation that new technologies can improve the accurate identification of crime patterns. Here, we explore big data technology and design a machine learning algorithm for forecasting repeated arrests. The forecasts are based on administrative data provided by the national Chilean police agencies, including a history of arrests in Santiago de Chile and personal metadata such as gender and age. Excellent algorithmic performance was achieved with various supervised machine learning techniques. Still, there are many challenges regarding the design of the mathematical model, and its eventual incorporation into predictive policing will depend upon better insights into the effectiveness and ethics of preemptive strategies. [ABSTRACT FROM AUTHOR]

In the wider spectrum of Taiwanese public service spheres, the herculean services and dedication of its committed Police personnel have long been recognized, respected, and admired. However, regrettably, question marks concerning their conduct, discipline, and abuse of power have surfaced on intermittent occasions. A classic example that lingers in the public memory is the bribing of Taiwanese video game companies to some unscrupulous elements of the police department, in the closing decades of the 20th century that triggered public outrage and called for scrutiny concerning serious lapses in the discipline and conduct of Police personnel. This research paper endeavors to understand, analyze and address some of those issues based on empirical data on the police personnel of certain specific work zones/areas taking into account holistically both the sentenced police officers vis-à-vis the law-abiding police officers. This module looks into and sieves through available data for seven critical variables, including their degree of variation through the Identification and Analysis Method to develop a Predictive Model on Police Ethics and the important factors that affect Police Ethics. Concretely based on the integrated research, it is proposed that this Predictive Model has good applicability as well as accurate predictive ability in addressing the core issues that affect Police Ethics. It is hoped that through this Early Warning Predictive Model--all the stakeholders that are Policy and Decision-makers, Regulatory Police Agencies but more importantly the Police personnel themselves would effectively address the criticality of the issues that affect the Police Ethics so as to undertake competent and effective measures to erase/lessen the menace and provide an early rehabilitative care/assistance to build a strong, constructive and visionary Taiwanese Police Force to meet the challenges of 21st century and beyond. [ABSTRACT FROM AUTHOR]

Predictive policing has its roots in crime hotspot modeling. In this paper we give an example of what goes into mathematical crime hotspot modeling and show that the modeling assumptions perpetuate systemic racism in policing. The goal of this paper is to raise objections to this field of research, not on its mathematical merit, but on the scope of the problem formation. We do not make any suggestions about how to incorporate race into predictive policing models in order to make them less biased. [ABSTRACT FROM AUTHOR]

The article deals with the use and implementation of predictive policing technologies (PPTs) in the prevention of organized crime. It explores the inherent logic and the criticisms that have been raised against PPTs such as privacy concerns and institutional discrimination within law enforcement practices. The development of technologies that target more complex tasks related to preventing organized crime is discussed, including machine learning-based crime analysis tools.

An introduction is presented in which the editor discusses articles in the issue on topics including the use of predictive policing technologies (PPTs) in crime prevention, concerns about opacity and bias in the use of artificial intelligence (AI) tools, and the use of AI tools to address rule of law failings.

How can we secure an accessible and open democratic debate about police use of predictive analytics when the technology itself is a specialized area of expertise? Police utilize technologies of prediction and automation where the underlying technology is often a machine learning (ML) model. The article argues that important issues concerning ML decision models can be unveiled without detailed knowledge about the learning algorithm, empowering non-ML experts and stakeholders in debates over if, and how to, include them, for example, in the form of predictive policing. Non-ML experts can, and should, review ML models. We provide a 'toolbox' of questions about three elements of a decision model that can be fruitfully scrutinized by non-ML experts: the learning data, the learning goal, and constructivism. Showing this room for fruitful criticism can empower non-ML experts and improve democratic accountability when using ML models in policing. [ABSTRACT FROM AUTHOR]

Predictive policing generally refers to police work that utilises strategies, algorithmic technologies, and big data to generate near-future predictions about the people and places deemed likely to be involved in or experience crime. Claimed benefits of predictive policing centre on the technology's ability to enable pre-emptive police work by automating police decisions. The goal is that officers will rely on computer software and smartphone applications to instruct them about where and who to police just as Uber drivers rely on similar technologies to instruct them about where to pick up passengers. Unfortunately, little is known about the experiences of the in-field users of predictive technologies. This article helps fill this gap by addressing the under researched area of how police officers engage with predictive technologies. As such, data is presented that outlines the findings of a qualitative study with UK police organisations involved in designing and trialing predictive policing software. Research findings show that many police officers have a detailed awareness of the limitations of predictive technologies, specifically those brought about by errors and biases in input data. This awareness has led many officers to develop a sceptical attitude towards predictive technologies and, in a few cases, these officers have expressed a reluctance to use predictive technologies. Based on these findings, this paper argues that claims about predictive software's ability to neutralise the subjectivity of police work overlooks the ongoing struggles of the police officer to assert their agency and mediate the extent to which predictions will be trusted and utilised. [ABSTRACT FROM AUTHOR]

Motivated by the classic work of Max Weber, this study develops an ideal type to study the transformation of government bureaucracy in the 'age of algorithms'. We present the new ideal type – the algocracy – and position this vis-à-vis three other ideal types (machine bureaucracy, professional bureaucracy, infocracy). We show that while the infocracy uses technology to improve the machine bureaucracy, the algocracy automates the professional bureaucracy. By reducing and quantifying the uncertainty of decision-making processes in organizations the algocracy rationalizes the exercise of rational-legal authority in the professional bureaucracy. To test the value of the ideal type, we use it to analyze the introduction of a predictive policing system in the Berlin police. Our empirical analysis confirms the value of the algocracy as a lens to study empirical practices: the study highlights how the KrimPro system conditions professional assessments and centralizes control over complex police processes. This research therefore positions the algocracy in the heart of discussions about the future of the public sector and presents an agenda for further research. [ABSTRACT FROM AUTHOR]

In the wider spectrum of Taiwanese public service spheres, the herculean services and dedication of its committed Police personnel have long been recognized, respected, and admired. However, regrettably, question marks concerning their conduct, discipline, and abuse of power have surfaced on intermittent occasions. A classic example that lingers in the public memory is the bribing of Taiwanese video game companies to some unscrupulous elements of the police department, in the closing decades of the 20th century that triggered public outrage and called for scrutiny concerning serious lapses in the discipline and conduct of Police personnel. This research paper endeavors to understand, analyze and address some of those issues based on empirical data on the police personnel of certain specific work zones/areas taking into account holistically both the sentenced police officers vis-à-vis the law-abiding police officers. This module looks into and sieves through available data for seven critical variables, including their degree of variation through the Identification and Analysis Method to develop a Predictive Model on Police Ethics and the important factors that affect Police Ethics. Concretely based on the integrated research, it is proposed that this Predictive Model has good applicability as well as accurate predictive ability in addressing the core issues that affect Police Ethics. It is hoped that through this Early Warning Predictive Model—all the stakeholders that are Policy and Decision-makers, Regulatory Police Agencies but more importantly the Police personnel themselves would effectively address the criticality of the issues that affect the Police Ethics so as to undertake competent and effective measures to erase/lessen the menace and provide an early rehabilitative care/assistance to build a strong, constructive and visionary Taiwanese Police Force to meet the challenges of 21st century and beyond. [ABSTRACT FROM AUTHOR]

In our country, the term Predictive Law Enforcement is confused with the preventive duties of law enforcement. One of the states that best implements preventive law enforcement is the Russian Federation. Preventive law enforcement is under the responsibility of all public institutions, nongovernmental organizations, universities and families. In the preventive law enforcement, law enforcement units, which have the authority to finally use public force, come into play. Predictive Law enforcement, however, is law enforcement that applies analytical techniques, especially quantitative techniques, to prevent crime by determining possible targets for law enforcement intervention or to solve past crimes by making statistical predictions. In this article, the crime prevention system of the Russian Federation and law enforcement practices are examined. "Preventive Law Enforcement - Law Enforcement Based on Foresight" and "Preventive Duties of Law Enforcement", which contain public power in the organization structure and which are accepted to be fulfilled on the basis of public law, are presented with their successful examples in the Russian Federation. The possible benefits of using these successful applications in terms of our country's law enforcement have been put forth. [ABSTRACT FROM AUTHOR]

For several years now, crime prediction software operating on the basis of data analysis and algorithmic pattern detection has been employed by police departments throughout the world. As these technologies aim at forestalling criminal events, they may aptly be understood as elements of preventive strategies. Do they also initiate a logic of preemptive policing, as several authors have suggested? Using the example of crime prediction software that is used in German-speaking countries, the article shows how current forms of predictive policing echo classical modes of risk calculation: usually employed in connection with domestic burglary, they help police to identify potential high-risk areas by extrapolating past crime patterns into the future. However, preemptive elements also emerge, to the extent that the software fosters 'possibilistic' thinking in police operations. Moreover, current advances in crime prediction technologies give us a quite different picture of a probable future of preemptive policing. Following a general trend of data-driven government that draws on self-learning algorithms and heterogeneous data sources, crime prediction software will likely be integrated into assemblages of predictive technologies where criminal events are indeed foreclosed before they can unfold and emerge, implying preemptive police action. [ABSTRACT FROM AUTHOR]

From the 1960s onwards, police scholars have accurately documented what patrol officers do when carrying out their job. Their work has been supplemented by miscellaneous publications on police culture, police working styles, and factors influencing police decision-making. Although scholars do recognise the significance of professional freedom, what takes place within this discretionary space to manoeuvre has never been fully studied, especially within the context of predictive policing. We aim to open up the 'black box' of police discretion by offering an empirical study on the everyday patrol work of a predictive policing flex-team (a kind of data-driven flying squad) in Amsterdam, the capital city of the Netherlands. Building on previous literature and on our own insights, we advance a theoretical model of police craftsmanship with particular interest in patrol officer-civilian encounters. This model consists of five, partly overlapping, routine actions: searching for relevant clues, defining a situation, anticipating a situation, dealing with civilians and closing a situation. A deeper insight into such routines will enable patrol officers to reflect on what 'good policing' entails and how to further professionalise their occupation against the complex backdrop of micro-grid crime prevention technology. [ABSTRACT FROM AUTHOR]

Despite extensive research, measurable benefits of predictive policing are scarce. We argue that powerful models might not always help the work of officers. Furthermore, developed models are often unexplainable, leading to trust issues between police intuition and machine-made prediction. We use a joint approach, mixing criminology and data science knowledge, to design an explainable predictive policing model. The proposed model (a set of explainable decision trees) can predict police resource requirement across the city and explain this prediction based on human-understandable cues (i.e., past event information, weather, and socio-demographic information). The explainable decision tree is then compared to a non-explainable model (i.e., a neural network) to compare performance. Analyzing the decision tree behaviour revealed multiple relations with established criminology knowledge. Weather and recent event distribution were found to be the most useful predictors of police workforce resource. Despite wide research showing relationships between socio-demographic information and police activity, socio-demographic information did not contribute much to the model's performance. Though there is a lack of research on measurable effects of predictive policing applications, we argue that combining human instinct with machine prediction reduces risks of human knowledge loss, machine bias, and lack of confidence in the system. [ABSTRACT FROM AUTHOR]