Suchergebnisse

Cover Das Böse in der politischen Theorie -- Inhalt -- Einleitung -- Das Gute als Norm der Freiheit bei Kant -- Über Freiheiten ohne vernünftigen Widerspruch. Platons, Augustinus' und Leibnizens ethischer Intellektualismus -- Das absolut Böse und das Scheitern der moralischen Freiheit zum Guten bei Kant -- Die anti-politische Aufhebung des Guten bei Nietzsche -- Hegels Moralkritik - Vorsittliches Gutes -- Machiavelli, Hobbes und Weber: Ethik der Macht -- Das Recht als Ende der Versuchung bei Kant -- Der Komplementärmythos vom edlen oder schlechten Wilden bei Hobbes und Rousseau -- Politik des ewigen Friedens gegen das evil empire bei Kant -- Die Macht der Negativa in der politischen Theorie Hegels -- Das (un)lebendige Gute der Sittlichkeit bei Hegel -- Abschluss -- Literatur.

Are algorithms ruling the world today? Is artificial intelligence making life-and-death decisions? Are social media companies able to manipulate elections? As we are confronted with public and academic anxieties about unprecedented changes, this book offers a different analytical prism to investigate these transformations as more mundane and fraught. Aradau and Blanke develop conceptual and methodological tools to understand how algorithmic operations shape the government of self and other. While disperse and messy, these operations are held together by an ascendant algorithmic reason. Through a global perspective on algorithmic operations, the book helps us understand how algorithmic reason redraws boundaries and reconfigures differences. The book explores the emergence of algorithmic reason through rationalities, materializations, and interventions. It traces how algorithmic rationalities of decomposition, recomposition, and partitioning are materialized in the construction of dangerous others, the power of platforms, and the production of economic value. The book shows how political interventions to make algorithms governable encounter friction, refusal, and resistance. The theoretical perspective on algorithmic reason is developed through qualitative and digital methods to investigate scenes and controversies that range from mass surveillance and the Cambridge Analytica scandal in the UK to predictive policing in the US, and from the use of facial recognition in China and drone targeting in Pakistan to the regulation of hate speech in Germany. Algorithmic Reason offers an alternative to dystopia and despair through a transdisciplinary approach made possible by the authors' backgrounds, which span the humanities, social sciences, and computer sciences.

Are algorithms ruling the world today? Is artificial intelligence making life-and-death decisions? Are social media companies able to manipulate elections? As we are confronted with public and academic anxieties about unprecedented changes, this book offers a different analytical prism to investigate these transformations as more mundane and fraught. Aradau and Blanke develop conceptual and methodological tools to understand how algorithmic operations shape the government of self and other. While disperse and messy, these operations are held together by an ascendant algorithmic reason. Through a global perspective on algorithmic operations, the book helps us understand how algorithmic reason redraws boundaries and reconfigures differences. The book explores the emergence of algorithmic reason through rationalities, materializations, and interventions. It traces how algorithmic rationalities of decomposition, recomposition, and partitioning are materialized in the construction of dangerous others, the power of platforms, and the production of economic value. The book shows how political interventions to make algorithms governable encounter friction, refusal, and resistance. The theoretical perspective on algorithmic reason is developed through qualitative and digital methods to investigate scenes and controversies that range from mass surveillance and the Cambridge Analytica scandal in the UK to predictive policing in the US, and from the use of facial recognition in China and drone targeting in Pakistan to the regulation of hate speech in Germany. Algorithmic Reason offers an alternative to dystopia and despair through a transdisciplinary approach made possible by the authors' backgrounds, which span the humanities, social sciences, and computer sciences.

Articulations of discontinuity and moments of dissent have been central to critical historical work. However, such vocabularies and analyses of historical change have received less attention in the emerging field of digital methods. Digital methods based on discerning patterns have focused on continuities, while discontinuities and ruptures have been derivative of trends and patterns. By contrast, genealogical methods attend to the entanglement of continuity and discontinuity, and focus on contingency and singularity. This article proposes to develop methods of computational genealogy to analyze multiple temporalities in historical discourses. We experiment with our proposed computational genealogy using the archive of Inaugural speeches by US presidents. In particular, we show that there is neither a linear advance to Trump's rhetoric nor an exceptional rupture. Our analysis shows that Trump's speech is much more the struggle of the Republicans with their own past ideas than struggles with Democrats.

AbstractAs digital technologies and algorithmic rationalities have increasingly reconfigured security practices, critical scholars have drawn attention to their performative effects on the temporality of law, notions of rights, and understandings of subjectivity. This article proposes to explore how the 'other' is made knowable in massive amounts of data and how the boundary between self and other is drawn algorithmically. It argues that algorithmic security practices and Big Data technologies have transformed self/other relations. Rather than the enemy or the risky abnormal, the 'other' is algorithmically produced as anomaly. Although anomaly has often been used interchangeably with abnormality and pathology, a brief genealogical reading of the concept shows that it works as a supplementary term, which reconfigures the dichotomies of normality/abnormality, friend/enemy, and identity/difference. By engaging with key practices of anomaly detection by intelligence and security agencies, the article analyses the materialisation of anomalies as specific spatial 'dots', temporal 'spikes', and topological 'nodes'. We argue that anomaly is not simply indicative of more heterogeneous modes of othering in times of Big Data, but represents a mutation in the logics of security that challenge our extant analytical and critical vocabularies.

From 'connecting the dots' and finding 'the needle in the haystack' to predictive policing and data mining for counterinsurgency, security professionals have increasingly adopted the language and methods of computing for the purposes of prediction. Digital devices and big data appear to offer answers to a wide array of problems of (in)security by promising insights into unknown futures. This paper investigates the transformation of prediction today by placing it within governmental apparatuses of discipline, biopower and big data. Unlike disciplinary and biopolitical governmentality, we argue that prediction with big data is underpinned by the production of a different time/space of 'between-ness'. The digital mode of prediction with big data reconfigures how we are governed today, which we illustrate through an analysis of how predictive policing actualises between-ness as hotspots and near-real-time decisions.

From 'connecting the dots' and finding 'the needle in the haystack' to predictive policing and data mining for counterinsurgency, security professionals have increasingly adopted the language and methods of computing for the purposes of prediction. Digital devices and big data appear to offer answers to a wide array of problems of (in)security by promising insights into unknown futures. This article investigates the transformation of prediction today by placing it within governmental apparatuses of discipline, biopower and big data. Unlike disciplinary and biopolitical governmentality, we argue that prediction with big data is underpinned by the production of a different time/space of 'between-ness'. The digital mode of prediction with big data reconfigures how we are governed today, which we illustrate through an analysis of how predictive policing actualizes between-ness as hotspots and near-real-time decisions.

The Snowden revelations and the emergence of 'Big Data' have rekindled questions about how security practices are deployed in a digital age and with what political effects. While critical scholars have drawn attention to the social, political and legal challenges to these practices, the debates in computer and information science have received less analytical attention. This paper proposes to take seriously the critical knowledge developed in information and computer science and reinterpret their debates to develop a critical intervention into the public controversies concerning data-driven security and digital surveillance. The paper offers a two-pronged contribution: on the one hand, we challenge the credibility of security professionals' discourses in light of the knowledge that they supposedly mobilize; on the other, we argue for a series of conceptual moves around data, human–computer relations, and algorithms to address some of the limitations of existing engagements with the Big Data-security assemblage.

The Snowden revelations and the emergence of 'Big Data' have rekindled questions about how security practices are deployed in a digital age and with what political effects. While critical scholars have drawn attention to the social, political and legal challenges to these practices, the debates in computer and information science have received less analytical attention. This paper proposes to take seriously the critical knowledge developed in information and computer science and reinterpret their debates to develop a critical intervention into the public controversies concerning data-driven security and digital surveillance. The paper offers a two-pronged contribution: on the one hand, we challenge the credibility of security professionals' discourses in light of the knowledge that they supposedly mobilize; on the other, we argue for a series of conceptual moves around data, human–computer relations, and algorithms to address some of the limitations of existing engagements with the Big Data-security assemblage.

International audience ; In recent years, European governments and funders, universities and academic societies have increasingly discovered the digital humanities as a new and exciting field that promises new discoveries in humanities research. The funded projects are, however, often ad hoc experiments and stand in isolation from other national and international work. What is lacking is an infrastructure to leverage these pioneering projects into systematic investigations, with methods and technical environments that can be taken up by others. The editors of this special issue are both directors of Digital Research Infrastructure for the Arts and Humanities (DARIAH), which aims to set up a virtual bridge between the many digital arts and humanities projects across Europe. DARIAH is being developed with an understanding that infrastructure does not need to imply a seemingly generic and neutral set of technologies, but should be based upon communities.

"In this paper the authors investigate the importance of research infrastructures for arts and humanities research. They seek to outline the development of a digital research infrastructure localized in the science and engineering domain and framed within the concept of e-Science. The authors define the primary characteristics of e-Science as big data and big structures such as the grid and high performance computing. They will attempt to demonstrate the transfer of the e-Science paradigm to the humanities and to assess what worked and what did not. They then suggest how thinking about technology and infrastructure through and within the humanities can lead to transformation and finish with a suggestion that the future for humanities research infrastructures is best framed around the emerging idea of a humanities specific digital ecosystem." (author's abstract)

Abstract
This article brings debates about data visualization in digital humanities in conversation with critical security studies and international relations. Building on feminist approaches in digital humanities, we explore the potential and limitations of data visualization as a critical method for research on (in)security. We unpack three aspects of making data visualizations by specifying "making" in this context as working, orienting, and critiquing. Making data visualizations as a methodological device is oriented by questions about the contestation of security and orients research by provoking new questions about practices of critique. Empirically, we situate data visualizations within British parliamentary debates about the Government Communications Headquarters (GCHQ), the UK's signals intelligence agency, which has garnered much public attention in the wake of the Snowden disclosures of transnational mass surveillance. We argue that data visualization in the parliamentary archive can destabilize dominant understandings of security, problematize narratives of security actors and oversight, and attend to the uneven presence of critique and contestation within and beyond parliamentary debates.

The opacity of digital technologies has posed significant challenges for critical research and digital methods. In response, controversy mapping, reverse engineering and hacking have been key methodological devices to grapple with opacity and 'open the black box' of digital ecosystems. We take recent developments in digital humanitarianism and the accelerated production of apps for refugees following the 2015 Mediterranean refugee crisis as a site of methodological experimentation to advance hacking as critical methodological interference. Drawing on the work of Michel Serres, we propose to understand digital technologies as 'parasitic' and reconceptualise hacking as 'acts of digital parasitism'. Acts of digital parasitism are interferences that work alongside rather than work against. On one hand, this reworking of hacking advances an agenda for digital methods through reworking hacking for digital humanities and social science research. On the other, it allows us to show how the object of research – humanitarian apps – is configured through platformisation and incorporation within digital parasitic relations.

In this paper, we devise a machine learning protocol to tackle a complex sociological task: to create a research sample from a few examples of interest, but in the absences of a clear definition of the target subset. As an example, we create a sample of organisational leaders starting from a list of nominees for the Bureau of the Intergovernmental Panel on Climate Change. The difficulty in this task lies in the impossibility to spell out the characteristics that define leadership in a complex and highly distributed organization like the IPCC. To bypass this lack of explicit definition, we use a series of techniques for anomaly detection to identify IPCC contributors with profiles similar to official Bureau nominees. We find that we can construct a precise (albeit implicit) model of IPCC leadership despite its social and political complexity, and that we can usefully use this model to expand our initial sample.