Michael McGuire and Thomas J Holt (eds) (2017) The Routledge Handbook of Technology, Crime and Justice. London, New York: Routledge

Not applicabl

A Story of Organized Crime: Constructing Criminality and Building Institutions

This is a narrative about the way in which a category of crime-to-be-combated is constructed through the discipline of criminology and the agents of discipline in criminal justice. The aim was to examine organized crime through the eyes of those whose job it is to fight it (and define it), and in doing so investigate the ways social problems surface as sites for state intervention. A genealogy of organized crime within criminological thought was completed, demonstrating that there are a range of different ways organized crime has been constructed within the social scientific discipline, and each of these were influenced by the social context, political winds and intellectual climate of the time. Following this first finding, in-depth qualitative interviews were conducted with individuals who had worked at the apex of the policing of organized crime in Australia, in order to trace their understandings of organized crime across recent history. It was found that organized crime can be understood as an object of the discourse of the politics of law and order, the discourse of international securitization, new public management in policing business, and involves the forging of outlaw identities. Therefore, there are multiple meanings of organized crime that have arisen from an interconnected set of social, political, moral and bureaucratic discourses. The institutional response to organized crime, including law and policing, was subsequently examined. An extensive legislative framework has been enacted at multiple jurisdictional levels, and the problem of organized crime was found to be deserving of unique institutional powers and configurations to deal with it. The social problem of organized crime, as constituted by the discourses mapped out in this research, has led to a new generation of increasingly preemptive and punitive laws, and the creation of new state agencies with amplified powers. That is, the response to organized crime, with a focus on criminalization and enforcement, has been driven and shaped by the four discourses and the way in which the phenomenon is constructed within them. An appreciation of the nexus between the emergence of the social problem, and the formation of institutions in response to it, is important in developing a more complete understanding of the various dimensions of organized crime

AI Ethics Needs Good Data

In this chapter we argue that discourses on AI must transcend the language of 'ethics' and engage with power and political economy in order to constitute 'Good Data'. In particular, we must move beyond the depoliticised language of 'ethics' currently deployed (Wagner 2018) in determining whether AI is 'good' given the limitations of ethics as a frame through which AI issues can be viewed. In order to circumvent these limits, we use instead the language and conceptualisation of 'Good Data', as a more expansive term to elucidate the values, rights and interests at stake when it comes to AI's development and deployment, as well as that of other digital technologies. Good Data considerations move beyond recurring themes of data protection/privacy and the FAT (fairness, transparency and accountability) movement to include explicit political economy critiques of power. Instead of yet more ethics principles (that tend to say the same or similar things anyway), we offer four 'pillars' on which Good Data AI can be built: community, rights, usability and politics. Overall we view AI's 'goodness' as an explicly political (economy) question of power and one which is always related to the degree which AI is created and used to increase the wellbeing of society and especially to increase the power of the most marginalized and disenfranchised. We offer recommendations and remedies towards implementing 'better' approaches towards AI. Our strategies enable a different (but complementary) kind of evaluation of AI as part of the broader socio-technical systems in which AI is built and deployed.Comment: 20 pages, under peer review in Pieter Verdegem (ed), AI for Everyone? Critical Perspectives. University of Westminster Pres

3D printing, policing and crime

This article examines the implications of advanced manufacturing technology, more commonly known as three dimensional (3D) printing, for policing and crime, notably the dissemination of digital design files and the use of 3D printers to produce illicit firearms. The application and rapid evolution of 3D printing technology has created new challenges for law and regulation, and represents an interesting security paradox, albeit one which until now has received scant attention in the criminological or policing literature. On the one hand, 3D printing denotes a significant shift in the creation and use of objects, ranging from food to body parts, and more controversially, weaponry. On the other hand, the use of this technology to create items such as firearms and weapons signifies a potential safety, security, and legal challenge. We explore the emergence of 3D printing and its use to create firearms along with the theoretical challenges to legal design and enforcement presented by this decentralised technology. We also present some empirical data on instances of 3D printed firearms and firearm parts being detected internationally, and some jurisdictions’ legal and policy responses. We conclude by considering that any regulation of 3D printed firearms must be based on a robust evidence base and take proper account of citizens’ rights, but also that any national regulation will be in tension with the transnational and decentralised nature of the technology

Azithromycin effectiveness against intracellular infections of Francisella

<p>Abstract</p> <p>Background</p> <p>Macrolide antibiotics are commonly administered for bacterial respiratory illnesses. Azithromycin (Az) is especially noted for extremely high intracellular concentrations achieved within macrophages which is far greater than the serum concentration. Clinical strains of Type B <it>Francisella </it>(<it>F.</it>) <it>tularensis </it>have been reported to be resistant to Az, however our laboratory <it>Francisella </it>strains were found to be sensitive. We hypothesized that different strains/species of <it>Francisella </it>(including Type A) may have different susceptibilities to Az, a widely used and well-tolerated antibiotic.</p> <p>Results</p> <p><it>In vitro </it>susceptibility testing of Az confirmed that <it>F. tularensis subsp. holarctica </it>Live Vaccine Strain (LVS) (Type B) was not sensitive while <it>F. philomiragia, F. novicida</it>, and Type A <it>F. tularensis </it>(NIH B38 and Schu S4 strain) were susceptible. In J774A.1 mouse macrophage cells infected with <it>F. philomiragia, F. novicida</it>, and <it>F. tularensis </it>LVS, 5 μg/ml Az applied extracellularly eliminated intracellular <it>Francisella </it>infections. A concentration of 25 μg/ml Az was required for <it>Francisella-</it>infected A549 human lung epithelial cells, suggesting that macrophages are more effective at concentrating Az than epithelial cells. Mutants of RND efflux components (<it>tolC </it>and <it>ftlC</it>) in <it>F. novicida </it>demonstrated less sensitivity to Az by MIC than the parental strain, but the <it>tolC </it>disc-inhibition assay demonstrated increased sensitivity, indicating a complex role for the outer-membrane transporter. Mutants of <it>acrA </it>and <it>acrB </it>mutants were less sensitive to Az than the parental strain, suggesting that AcrAB is not critical for the efflux of Az in <it>F. novicida</it>. In contrast, <it>F. tularensis </it>Schu S4 mutants Δ<it>acrB </it>and Δ<it>acrA </it>were more sensitive than the parental strain, indicating that the AcrAB may be important for Az efflux in <it>F. tularensis </it>Schu S4. <it>F. novicida </it>LPS O-antigen mutants (<it>wbtN, wbtE, wbtQ </it>and <it>wbtA</it>) were found to be less sensitive <it>in vitro </it>to Az compared to the wild-type. Az treatment prolonged the survival of <it>Galleria </it>(<it>G</it>.) <it>mellonella </it>infected with <it>Francisella</it>.</p> <p>Conclusion</p> <p>These studies demonstrate that Type A <it>Francisella </it>strains, as well as <it>F. novicida </it>and <it>F. philomiragia</it>, are sensitive to Az <it>in vitro. Francisella </it>LPS and the RND efflux pump may play a role in Az sensitivity. Az also has antimicrobial activity against intracellular <it>Francisella</it>, suggesting that the intracellular concentration of Az is high enough to be effective against multiple strains/species of <it>Francisella</it>, especially in macrophages. Az treatment prolonged survival an <it>in vivo </it>model of <it>Francisella-</it>infection.</p

AI ethics needs good data

In this chapter we argue that discourses on AI must transcend the language of 'ethics' and engage with power and political economy in order to constitute 'Good Data'. In particular, we must move beyond the depoliticised language of 'ethics' currently deployed (Wagner 2018) in determining whether AI is 'good' given the limitations of ethics as a frame through which AI issues can be viewed. In order to circumvent these limits, we use instead the language and conceptualisation of 'Good Data', as a more expansive term to elucidate the values, rights and interests at stake when it comes to AI's development and deployment, as well as that of other digital technologies. Good Data considerations move beyond recurring themes of data protection/privacy and the FAT (fairness, transparency and accountability) movement to include explicit political economy critiques of power. Instead of yet more ethics principles (that tend to say the same or similar things anyway), we offer four 'pillars' on which Good Data AI can be built: community, rights, usability and politics. Overall we view AI's 'goodness' as an explicly political (economy) question of power and one which is always related to the degree which AI is created and used to increase the wellbeing of society and especially to increase the power of the most marginalized and disenfranchised. We offer recommendations and remedies towards implementing 'better' approaches towards AI. Our strategies enable a different (but complementary) kind of evaluation of AI as part of the broader socio-technical systems in which AI is built and deployed

The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism

Computer vision and other biometrics data science applications have commenced a new project of profiling people. Rather than using 'transaction generated information', these systems measure the 'real world' and produce an assessment of the 'world state' - in this case an assessment of some individual trait. Instead of using proxies or scores to evaluate people, they increasingly deploy a logic of revealing the truth about reality and the people within it. While these profiling knowledge claims are sometimes tentative, they increasingly suggest that only through computation can these excesses of reality be captured and understood. This article explores the bases of those claims in the systems of measurement, representation, and classification deployed in computer vision. It asks if there is something new in this type of knowledge claim, sketches an account of a new form of computational empiricism being operationalised, and questions what kind of human subject is being constructed by these technological systems and practices. Finally, the article explores legal mechanisms for contesting the emergence of computational empiricism as the dominant knowledge platform for understanding the world and the people within it

Artificial Intelligence Governance and Ethics : Global Perspectives

Artificial intelligence (AI) is a technology which is increasingly being utilised in society and the economy worldwide, and its implementation is planned to become more prevalent in coming years. AI is increasingly being embedded in our lives, supplementing our pervasive use of digital technologies. But this is being accompanied by disquiet over problematic and dangerous implementations of AI, or indeed, even AI itself deciding to do dangerous and problematic actions, especially in fields such as the military, medicine and criminal justice. These developments have led to concerns about whether and how AI systems adhere, and will adhere to ethical standards. These concerns have stimulated a global conversation on AI ethics, and have resulted in various actors from different countries and sectors issuing ethics and governance initiatives and guidelines for AI. Such developments form the basis for our research in this report, combining our international and interdisciplinary expertise to give an insight into what is happening in Australia, China, Europe, India and the US