Search

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]

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.

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]

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]

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]

Copyright of IDP: Revista de Internet, Derecho y Politica is the property of Universitat Oberta de Catalunya 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.)

Copyright of IDP: Revista de Internet, Derecho y Politica is the property of Universitat Oberta de Catalunya 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.)

We have derived conditions when unintended feedback loops occur in supervised machine learning systems. In this paper, we study an important problem of discovering and measuring hidden feedback loops. Such feedback loops occur in web search, recommender systems, healthcare, predictive public policing and other systems. As a possible cause of echo chambers and filter bubbles, these feedback loops tend to produce concept drifts in user behavior. We study systems in their context of use, because both learning algorithms and user interactions are important. Then we decompose the automation bias from the use of the system into users adherence to predictions and their usage rate to derive conditions for a feedback loop to occur. We also provide estimates for the size of a concept drift caused by the loop. A series of controlled simulation experiments with real-world and synthetic data support our findings. This paper builds on our prior results and elaborates the analytical model of feedback loops, extends the experiments, and provides practical application guidelines. [ABSTRACT FROM AUTHOR]

Recent years have witnessed an explosion of interest in predictive policing, with a clear opposition emerging between supporters and critics of its implementation. While critical accounts conventionally centre on opacities and operational asymmetries of the algorithmic construct (biased training, feedback loop, etc.), I argue that a different critique is first needed. Focussing on place-based techniques, I maintain that contemporary predictive mapping basically perpetuates the political and epistemic dictates which have historically framed the conceptualisation of crime in relation to space. Through a review of sources spanning from the Cartographic School to current predictive policing literature, I identify two main conceptual axes which operationalise this heritage: first, an explanatory framework of crime that has never detached from the socio-economic deficit archetype; and secondly, an ontologisation of crime alternative to biologicist positivism, nonetheless integral to the etiologic paradigm. Therefore, without first disputing these ideological bottlenecks, no initiative towards a transparent use of predictive policing is plausible, neither does a sharp distinction between place-based and person-based predictions seem tenable. En los últimos años se ha asistido a una explosión de interés por la vigilancia policial predictiva y al surgimiento de una clara oposición entre partidarios y detractores de su implementación. Mientras que los relatos críticos se centran convencionalmente en opacidades y asimetrías operacionales del constructo algorítmico (entrenamiento sesgado, bucle de retroalimentación, etc.), lo que aquí se plantea es la necesidad preliminar de otro tipo de crítica. Con el foco puesto en las técnicas de predicción geoespacial, se sostiene que los métodos de mapeo predictivo tienden a perpetuar los dictados políticos y epistemológicos que históricamente han enmarcado la conceptualización del delito en su relación con el espacio. Mediante una revisión de fuentes que abarcan desde la Escuela Cartográfica a la literatura contemporánea sobre vigilancia predictiva, se detectan dos grandes ejes conceptuales que operativizan dicho legado: primero, un marco explicativo del delito que nunca supo emanciparse del arquetipo del déficit socioeconómico, y segundo, una ontologización del delito alternativa a la del positivismo biologicista pero aun así parte integral del paradigma etiológico. Así pues, sin antes cuestionar dichas constricciones ideológicas, ninguna iniciativa encaminada a un uso transparente de la policía predictiva es plausible, ni una distinción real entre mapeo predictivo y predicción individualizada parece defendible. Available from: https://doi.org/10.35295/osls.iisl/0000-0000-0000-1360

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]

In the digital age, the use of advanced technology is becoming a new paradigm in police work, criminal justice, and the penal system. Algorithms promise to predict delinquent behaviour, identify potentially dangerous persons, and support crime investigation. Algorithm-based applications are often deployed in this context, laying the groundwork for a 'smart criminal justice'. In this qualitative study based on 32 interviews with criminal justice and police officials, we explore the reasons why and extent to which such a smart criminal justice system has already been established in Switzerland, and the benefits perceived by users. Drawing upon this research, we address the spread, application, technical background, institutional implementation, and psychological aspects of the use of algorithms in the criminal justice system. We find that the Swiss criminal justice system is already significantly shaped by algorithms, a change motivated by political expectations and demands for efficiency. Until now, algorithms have only been used at a low level of automation and technical complexity and the levels of benefit perceived vary. This study also identifies the need for critical evaluation and research-based optimization of the implementation of advanced technology. Societal implications, as well as the legal foundations of the use of algorithms, are often insufficiently taken into account. By discussing the main challenges to and issues with algorithm use in this field, this work lays the foundation for further research and debate regarding how to guarantee that 'smart' criminal justice is actually carried out smartly. [ABSTRACT FROM AUTHOR]

This article explores the shifting perceptual scales of racial epistemology and anti-blackness in predictive policing technology. Following Paul Gilroy, I argue that the historical production of racism and anti-blackness has always been deeply entwined with questions of scale and perception. Where racialisation was once bound to the anatomical scale of the body, Thao Than and Scott Wark’s conceptualisation of “racial formations as data formations” inform insights into the ways in which “race”, or its 21st century successor, is increasingly being produced as a cultivation of post-visual, data-driven abstractions. I build upon analysis of this phenomena in the context of predictive policing, where analytically derived “patrol zones” produce virtual barriers that divide civilian from suspect. Beyond a “garbage in, garbage out” critique, I explore the ways in which predictive policing instils racialisation as an epiphenomenon of data-generated proxies. By way of conclusion, I analyse American Artist’s 21-minute video installation 2015 (2019), which depicts the point of view of a police patrol car equipped with a predictive policing device, to parse the scales upon which algorithmic regimes of racial domination are produced and resisted.

Abstract Near-repeat victimization patterns have made predictive models for burglaries possible. While the models have been implemented in different countries, the results obtained have not always been in line with initial expectations; to the point where their real effectiveness has been called into question. The ability to predict crime to improve preventive policing strategies is still under study. This study aims to discover the limitations to and the success of the models that attempt to predict burglaries based on spatiotemporal patterns of the risk of break-ins spreading in geographic proximity to the initial break-ins. A spatiotemporal log-Gaussian Cox process is contemplated to model the generic near-repeat victimization scenario and adjusted using the Integrated Nested Laplace Approximation (INLA) methodology. This approach is highly suitable for studying and describing the near-repeat phenomenon. However, predictions obtained with INLA are quite monotonous, of low variability and do not reproduce well the local and short-term dynamics of burglaries for predictive purposes. The conclusion is that predictive models cannot be restricted exclusively to distance decay risk, but they must be designed to detect other types of spatiotemporal patterns which, among other possibilities, open up the possibility of correlating distant events and clusters. Although other studies have already highlighted this problem, the proposal here is to go one step further and clearly extend the near-repeat spatial patterns to achieve better prediction results.

Studies in the United States and Europe have demonstrated that burglary and vehicle crime exhibit consistent patterns, supporting the application of crime prediction techniques to proactively deploy police resources to reduce incidents of crime. Research into whether these techniques are applicable in an Australian context is currently limited. Using crime data from the Queensland Police Service, this study assessed the presence of spatio-temporal patterns in burglary, theft of motor vehicle and theft from motor vehicle offences in three distinct local government areas. After establishing the presence of spatio-temporal clustering, the forecasting performance of two predictive algorithms and a retrospective crime mapping technique was evaluated. Forecasting performance varied across study regions; however, the prediction algorithms performed as well as or better than the retrospective method, while using less data. The next step in evaluating predictive policing within Australia is to consider and design effective tactical responses to prevent crime based on the forecast locations and identified patterns. [ABSTRACT FROM AUTHOR]

Featured Application: The study synthesizes qualitative analysis to (1) highlight potential societal benefits and the ethical concerns of artificial intelligence (AI) technologies in policing, and (2) inform the responsible design and integration of AI technologies in law enforcement. Integrating artificial intelligence (AI) technologies into law enforcement has become a concern of contemporary politics and public discourse. In this paper, we qualitatively examine the perspectives of AI technologies based on 20 semi-structured interviews of law enforcement professionals in North Carolina. We investigate how integrating AI technologies, such as predictive policing and autonomous vehicle (AV) technology, impacts the relationships between communities and police jurisdictions. The evidence suggests that police officers maintain that AI plays a limited role in policing but believe the technologies will continue to expand, improving public safety and increasing policing capability. Conversely, police officers believe that AI will not necessarily increase trust between police and the community, citing ethical concerns and the potential to infringe on civil rights. It is thus argued that the trends toward integrating AI technologies into law enforcement are not without risk. Policymaking guided by public consensus and collaborative discussion with law enforcement professionals must aim to promote accountability through the application of responsible design of AI in policing with an end state of providing societal benefits and mitigating harm to the populace. Society has a moral obligation to mitigate the detrimental consequences of fully integrating AI technologies into law enforcement. [ABSTRACT FROM AUTHOR]

Copyright of Sexualidad, Salud y Sociedad: Revista Latinoamericana is the property of Editora da Universidade do Estado do Rio de Janeiro (EdUERJ) 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.)

Copyright of Quaestio Iuris (QI) is the property of Editora da Universidade do Estado do Rio de Janeiro (EdUERJ) 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.)

Near-repeat victimization patterns have made predictive models for burglaries possible. While the models have been implemented in different countries, the results obtained have not always been in line with initial expectations; to the point where their real effectiveness has been called into question. The ability to predict crime to improve preventive policing strategies is still under study. This study aims to discover the limitations to and the success of the models that attempt to predict burglaries based on spatiotemporal patterns of the risk of break-ins spreading in geographic proximity to the initial break-ins. A spatiotemporal log-Gaussian Cox process is contemplated to model the generic near-repeat victimization scenario and adjusted using the Integrated Nested Laplace Approximation (INLA) methodology. This approach is highly suitable for studying and describing the near-repeat phenomenon. However, predictions obtained with INLA are quite monotonous, of low variability and do not reproduce well the local and short-term dynamics of burglaries for predictive purposes. The conclusion is that predictive models cannot be restricted exclusively to distance decay risk, but they must be designed to detect other types of spatiotemporal patterns which, among other possibilities, open up the possibility of correlating distant events and clusters. Although other studies have already highlighted this problem, the proposal here is to go one step further and clearly extend the near-repeat spatial patterns to achieve better prediction results. [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]

This article examines Operation Migrant, initiated by the Norwegian police following the so-called migration crises in Europe in 2015. One of its central aims was, by predicting challenges related to increased migration, to improve resource allocation and prevent crime. By drawing on research on risk and threat assessment as a form of power, this article aims to analyze how risk categories are distributed and translated into a multilayered institutional arrangement where migration is policed as a potential crime. The article examines the indicators that the risk assessments are based on and the measures applied and investigates how discretionary practices make immigrants objects for law enforcement and policing. The article contributes to research on migration control in an ordinary police context, where immigration identity checks become part of the crime reduction strategy. Applying the concept of interpretive flexibility (Collins 1981), I will identify the steps in this chain of translation to explore the leap from targeting potentially criminal asylum seekers to targeting broader groups with temporary residency in Norway. The article analyzes the conditions determining how policing, technologies, and migrants are “co-constructed” in a chain of mediation and translation, which reinforces the view of migrants as risky and criminal. The final section discusses how risk and threat analysis is affected by the notion of the “crimmigrant other” (Franko 2020). In Norway, selectively targeting unwanted migrants as criminals has become dominant in police decision-making at a policy level and everyday practices affecting not only third country nationals but also unwanted eastern Europeans.

Simon Egbert & Matthias Leese: Criminal Futures. Predictive Policing and Everyday Police Work (Routledge, 2021), 231 Seiten. Eine Rezension / a review von Nikolaus Poechhacker. Egbert & Leese (2021) liefern mit ihrer umfassenden Studie über Predictive Policing einen wichtigen Beitrag zur Diskussion über die fortschreitende Digitalisierung und Algorithmisierung der Polizeiarbeit. Ziel des Buches ist es, Predictive Policing als eine Reihe zusammenhängender sozio-technischer Praktiken zu verstehen. Die Autoren diskutieren umfangreiches empirisches Material im Kontext der Akteur-Netzwerk-Theorie (ANT) und versuchen, Erkenntnisse aus der (kritischen) Kriminologie, der Soziologie und den Wissenschafts- und Technologiestudien (STS) miteinander zu verbinden. Während des gesamten Buches folgen die Autoren dem Fluss der Daten und den verschiedenen Akteuren durch den Prozess der Entwicklung und Durchführung von Predictive Policing. Durch eine konsequente Darstellung der unterschiedlichen Übersetzungsschritte stellen die Autoren klar dar, wie prädiktive Polizeiarbeit die Praxis der Polizeiarbeit verändert. Insbesondere der vorgeschlagene STS-Ansatz ist sehr überzeugend und fügt dem Feld eine neue und wichtige Perspektive hinzu.

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]

Die Erkenntnis, dass Algorithmen diskriminieren, benachteiligen und ausschließen, ist mittlerweile weit verbreitet und anerkannt. Programme zur Verwendung im predictive policing, zur Berechnung von Rückfälligkeitswahrscheinlichkeiten bei Straftäter:innen oder zur automatischen Gesichtserkennung diskriminieren vor allem gegen nicht-Weiße Menschen. Im Zuge dieser Erkenntnis wird auch vereinzelt die Verbindung zu epistemischer Ungerechtigkeit hergestellt, wobei die meisten Beiträge die Verbindungen zwischen Algorithmen und epistemischer Ungerechtigkeit nicht im Detail analysieren. Dieser Artikel unternimmt einen Versuch, diese Lücke in der Literatur zu verkleinern. Dabei umreiße ich zunächst das Feld der Algorithmen, um so die Entitäten, die epistemisch ungerecht sein könnten, klar zu fassen und zu unterscheiden. Dann erläutere ich eine Auswahl von epistemischen Ungerechtigkeiten, die für die Analyse von ungerechten Algorithmen relevant sind. Schließlich führe ich epistemische Ungerechtigkeiten und Algorithmen zusammen und zeige anhand von drei Beispielen – automatische Geschlechtserkennung, Googles Suchmaschinen-Algorithmus, PredPol (predictive policing) – dass Algorithmen an testimonialer Ungerechtigkeit und hermeneutischer Ungerechtigkeit beteiligt sind. Sie tragen so zudem auf verschiedenen Ebenen zur Exklusion von marginalisierten Gruppen bei.

The article addresses human rights requirements for person-based predictive policing. It looks into human rights standards, as elaborated in the selected European Court of Human Rights case law on creating police databases, watchlists and registries, and the police’s use of new technologies. The article argues that in the case of new technologies deployed by law enforcement the availability of evidence on the effectiveness and accuracy of a given method should be essential to assess that an interference with a human right using this technology is ‘necessary in a democratic society’. The article notes that the Court’s unwillingness to assess the claims about the utility of technology critically might suggest that its evaluation of human rights compliance of person-based predictive policing and other experimental technologies would suffer from a severe blind spot.

Digital prediction tools increasingly complement or replace other practices of coping with an uncertain future. The current COVID-19 pandemic, it seems, is further accelerating the spread of prediction. The prediction of the pandemic yields a pandemic of prediction. In this paper, we explore this dynamic, focusing on contagion models and their transmission back and forth between two domains of society: public health and public safety. We connect this movement with a fundamental duality in the prevention of contagion risk concerning the two sides of being-at-risk and being-a-risk. Both in the spread of a disease and in the spread of criminal behavior, a person at risk can be a risk to others and vice versa. Based on key examples, from this perspective we observe and interpret a circular movement in three phases. In the past, contagion models have moved from public health to public safety, as in the case of the Strategic Subject List used in the policing activity of the Chicago Police Department. In the present COVID-19 pandemic, the analytic tools of policing wander to the domain of public health – exemplary of this movement is the cooperation between the data infrastructure firm Palantir and the UK government’s public health system NHS. The expectation that in the future the predictive capacities of digital contact tracing apps might spill over from public health to policing is currently shaping the development and use of tools such as the Corona-Warn-App in Germany. In all these cases, the challenge of pandemic governance lies in managing the connections and the exchanges between the two areas of public health and public safety while at the same time keeping the autonomy of each.

This article examines the history of technological policing in Germany from the 1970s to today. Combining perspectives from intellectual history and legal theory, it explores the ideas and practices of Horst Herold, former President of the German Federal Criminal Police Agency. Herold's thought was both deeply influenced by cybernetics, a form of techno-utopian thought developed during World War II as well as the basis on which technological policing was first put to the test in Germany. The article illustrates Herold's hopes for a cybernetic transformation of police, law, and society. It locates Herold's cybernetic legal theory within a broader context of shifting legal paradigms of German public law towards the so-called preventive state in the 1970s and 80s. Crucially, an anti-legal affect is revealed to lie at the center of Herold's ideas. His concept of cybernetics ultimately serves to supplant the rule of law. In the concluding part, the article assesses Herold's legacy and attempts to both critique it and point towards a productive way forward by invoking modern, second-order order cybernetics. The article argues—perhaps counterintuitively—that Herold's ultimate failure was not adapting cybernetics but rather not staying with it all the way, supplanting its weaknesses, and drawing on its strengths. [ABSTRACT FROM AUTHOR]

Machine Learning has become a popular tool in a variety of applications in criminal justice, including sentencing and policing. Media has brought attention to the possibility of predictive policing systems causing disparate impacts and exacerbating social injustices. However, there is little academic research on the importance of fairness in machine learning applications in policing. Although prior research has shown that machine learning models can handle some tasks efficiently, they are susceptible to replicating systemic bias of previous human decision-makers. While there is much research on fair machine learning in general, there is a need to investigate fair machine learning techniques as they pertain to the predictive policing. Therefore, we evaluate the existing publications in the field of fairness in machine learning and predictive policing to arrive at a set of standards for fair predictive policing. We also review the evaluations of ML applications in the area of criminal justice and potential techniques to improve these technologies going forward. We urge that the growing literature on fairness in ML be brought into conversation with the legal and social science concerns being raised about predictive policing. Lastly, in any area, including predictive policing, the pros and cons of the technology need to be evaluated holistically to determine whether and how the technology should be used in policing. [ABSTRACT FROM AUTHOR]

This essay examines the Super Bowl and the smart city as conjoined spectacles. A focused case study on Super Bowl LIII and its staging in Atlanta, Georgia in 2019 allows us to investigate how the use of cutting-edge smart technologies, including cameras, sensors, artificial intelligence, image recognition, and data collection techniques to secure Mercedes Benz stadium naturalizes a broader anticipatory logic of state and corporate intervention, often evoked in the name of public safety and terrorism-prevention. Together the spectacles of sport and smart technologies gloss over systemic inequality and legitimize security infrastructures as well as related ideas that social problems such as a lack of affordable housing, police brutality, and environmental degradation are best addressed through technological solutions. Foregrounding the conjoined spectacles of the smart city and Super Bowl problematizes seemingly necessary security processes and social relations among people, events, technologies, and cities, inviting further research and discussions necessary for strengthening critical interventions and theorizing in these areas.

Abstract Background Predictive policing and crime analytics with a spatiotemporal focus get increasing attention among a variety of scientific communities and are already being implemented as effective policing tools. The goal of this paper is to provide an overview and evaluation of the state of the art in spatial crime forecasting focusing on study design and technical aspects. Methods We follow the PRISMA guidelines for reporting this systematic literature review and we analyse 32 papers from 2000 to 2018 that were selected from 786 papers that entered the screening phase and a total of 193 papers that went through the eligibility phase. The eligibility phase included several criteria that were grouped into: (a) the publication type, (b) relevance to research scope, and (c) study characteristics. Results The most predominant type of forecasting inference is the hotspots (i.e. binary classification) method. Traditional machine learning methods were mostly used, but also kernel density estimation based approaches, and less frequently point process and deep learning approaches. The top measures of evaluation performance are the Prediction Accuracy, followed by the Prediction Accuracy Index, and the F1-Score. Finally, the most common validation approach was the train-test split while other approaches include the cross-validation, the leave one out, and the rolling horizon. Limitations Current studies often lack a clear reporting of study experiments, feature engineering procedures, and are using inconsistent terminology to address similar problems. Conclusions There is a remarkable growth in spatial crime forecasting studies as a result of interdisciplinary technical work done by scholars of various backgrounds. These studies address the societal need to understand and combat crime as well as the law enforcement interest in almost real-time prediction. Implications Although we identified several opportunities and strengths there are also some weaknesses and threats for which we provide suggestions. Future studies should not neglect the juxtaposition of (existing) algorithms, of which the number is constantly increasing (we enlisted 66). To allow comparison and reproducibility of studies we outline the need for a protocol or standardization of spatial forecasting approaches and suggest the reporting of a study’s key data items.

Integrating artificial intelligence (AI) technologies into law enforcement has become a concern of contemporary politics and public discourse. In this paper, we qualitatively examine the perspectives of AI technologies based on 20 semi-structured interviews of law enforcement professionals in North Carolina. We investigate how integrating AI technologies, such as predictive policing and autonomous vehicle (AV) technology, impacts the relationships between communities and police jurisdictions. The evidence suggests that police officers maintain that AI plays a limited role in policing but believe the technologies will continue to expand, improving public safety and increasing policing capability. Conversely, police officers believe that AI will not necessarily increase trust between police and the community, citing ethical concerns and the potential to infringe on civil rights. It is thus argued that the trends toward integrating AI technologies into law enforcement are not without risk. Policymaking guided by public consensus and collaborative discussion with law enforcement professionals must aim to promote accountability through the application of responsible design of AI in policing with an end state of providing societal benefits and mitigating harm to the populace. Society has a moral obligation to mitigate the detrimental consequences of fully integrating AI technologies into law enforcement.

The article addresses human rights requirements for person-based predictive policing. It looks into human rights standards, as elaborated in the selected European Court of Human Rights case law on creating police databases, watchlists and registries, and the police's use of new technologies. The article argues that in the case of new technologies deployed by law enforcement the availability of evidence on the effectiveness and accuracy of a given method should be essential to assess that an interference with a human right using this technology is 'necessary in a democratic society'. The article notes that the Court's unwillingness to assess the claims about the utility of technology critically might suggest that its evaluation of human rights compliance of person-based predictive policing and other experimental technologies would suffer from a severe blind spot. [ABSTRACT FROM AUTHOR]

Copyright of Kommunikation@gesellschaft is the property of Kommunikation@gesellschaft 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.)

The journey to understand technological and digital policing requires a re-engagement with the most basic and widely used technology – paper-based registers for preventive policing. In the name of preventive policing, people from ex-untouchable castes, indigenous populations, and immigrants (in the city) are put under surveillance and recorded in registers. In the process, they earn criminal records for petty crimes, but also for no crimes at all. The registers enable a very ‘visible’ surveillance, where the ‘suspects’ are watched, followed and asked to come for mandatory attendance at the stations. Keeping in mind the segregated nature of the urban landscapes of cities in India, this is only possible for people who belong to certain strata of society and who do not have the privilege of escaping the prying eyes of the police. Researchers have argued that this form of policing is anti-poor or anti-marginalised. However, in this article, I argue that this form of preventive policing is better understood as being anti-caste. I demonstrate how police manuals, including guidelines for police record keeping and surveillance practices, reproduce and imitate the caste based social structure of India by using legacy practices from some still operational and some defunct laws. The paper-based registers maintain an illusion of objectivity – while the police can simply claim to be obeying the manuals. However, by enabling the recording of only those able to be visibly surveilled, those arrested for petty crimes, or those unable to escape the criminal justice system because of lack of money or social support or both, the paper-based registers become a vehicle of policing caste. By marking those thus recorded as habitual offenders, these registers propagate the caste-based understanding that defines crime as an inherent/hereditary trait of the lower castes. Prediction becomes nothing more than a self-fulfilling prophecy.

Road traffic collisions are responsible for 1.35 million deaths per year in the World. Moreover, they are the main cause of death for young people. To reduce the hazardous impact of road traffic crashes, it is necessary to constantly improve road traffic safety policing. Predictive Policing is a new concept that seems to be effective in fighting crime.For this background, the objective of this review was to identify studies that used methods of predictive policing in the context of road traffic safety. The systematic search of four databases identified a total of six original research articles. The analysis of the included articles revealed that the predictive policing techniques of hot spot analysis and Risk Terrain Modeling (RTM) are used in research to identify possible high risk areas for future road traffic crashes. The best results were achieved by methods combining both hot spot analysis and RTM. However, there are no studies examining the effectiveness of preventive measures taken based on these models, thus the practical utility of the use of predictive policing is still an open question. Furthermore, no papers were found implementing other models or methods of predictive policing in the context of road traffic safety. In the interdisciplinary context of crime and crash prediction, the methodological and theoretical potential of predictive policing should be further explored. The integrated approach will be an effective way in fighting crime and crashes in the light of limited resources and increasing workload in law enforcement agencies.