Fatal portraits: The selfie as agent of radicalization

For the modern-day jihadist, the digital self-portrait or, more specifically, battlefield selfie is a popular tool for identity building. Similarly to the selfies taken by non-violent practitioners of self-capture culture, the jihadist selfie represents an alternative to the Cartesian formulation of a unitary and indivisible self. Rather, it is a product of social relations and performative actions, constituted in dialogue with others through very specific socio-cultural frameworks and expectations. However, unlike its non-violent Doppelgänger, the expectations of this dialogue are centred around a larger political agenda which actively seeks to reformat collective memories of imperial Islamic conquests and co-opt religion as a way to impose a moral order on its violence. Importantly, the battlefield selfie allows the jihadist easily to traverse the boundaries between two seemingly opposing belief systems. Although there exists a wealth of scholarship of self-capture culture, image sharing sites and micro-celebritism, their pervasive influence and practice on battlefield is understudied. This article draws from the personal histories of key Islamic extremists who were both lionized and demonized as a result of their battlefield micro-influencer practices. Today, however, the same individuals can achieve internet fame by participating in self-capture culture – posting selfies, videos or blogging. In other words, never before has a soldier’s public communication been so personal yet collective.    &nbsp

Improving Bayesian statistics understanding in the age of Big Data with the bayesvl R package

The exponential growth of social data both in volume and complexity has increasingly exposed many of the shortcomings of the conventional frequentist approach to statistics. The scientific community has called for careful usage of the approach and its inference. Meanwhile, the alternative method, Bayesian statistics, still faces considerable barriers toward a more widespread application. The bayesvl R package is an open program, designed for implementing Bayesian modeling and analysis using the Stan language’s no-U-turn (NUTS) sampler. The package combines the ability to construct Bayesian network models using directed acyclic graphs (DAGs), the Markov chain Monte Carlo (MCMC) simulation technique, and the graphic capability of the ggplot2 package. As a result, it can improve the user experience and intuitive understanding when constructing and analyzing Bayesian network models. A case example is offered to illustrate the usefulness of the package for Big Data analytics and cognitive computing

The machine that ate bad people: The ontopolitics of the precrime assemblage

This article examines the “aesthetic” and “prescient” turn in the surveillant assemblage and the various ways in which risk technologies in local law enforcement are reshaping the post hoc traditions of the criminal justice system. The rise of predictive policing and crime prevention software illustrate not only how the world of risk management solutions for public security is shifting from sovereign borders to inner-city streets but also how the practices of authorization are allowing software systems to become proxy forms of sovereign power. The article also examines how corporate strategies and law enforcement initiatives align themselves through media, connectivity, and consumer-oriented opt-in strategies that endeavor to “mold” and “deputize” ordinary individuals into obedient and patriotic citizens

Innverview: Susan Carruthers

Innerview: Susan Carruthers Professor Susan Carruthers (Rutgers University), author of the book Media at War, talks to The Vision Machine about the historical and contemporary dimensions of the media in US warfare. This Innerview with Susan was recorded by Seb Kaempf and Peter Mantello in 2010. http://thevisionmachine.com/2013/03/innerview-with-susan-carruthers-media-and-us-warfare

An analytical framework for studying attitude towards emotional AI: The three-pronged approach

Emotional artificial intelligence (AI) is a narrow, weak form of an AI system that reads, classifies, and interacts with human emotions. This form of smart technology has become an integral layer of our digital and physical infrastructures and will radically transform how we live, learn, and work. Not only will emotional AI provide numerous benefits (i.e., increased attention and awareness, optimized productivity, stress management, etc.), but in sensing and interacting with our intimate emotions, it seeks to surreptitiously modify human behaviors. This study proposes to bring together the Technological Acceptance Model (TAM) and the Moral Foundation Theory to study determinants of emotional AI's acceptance under the analytical framework of the Three-pronged Approach (Contexts, Variables, and Statistical models). We argue that to quantitatively study the acceptance of new technologies, it is necessary to leverage two intuitions. The first is the degree of acceptance increases with how users of smart technology perceive its utilities and ease of use (formalized in the TAM). The second is the degree of acceptance decreases with the user's perception of threat or affirmation posed by the technology in relation to social norms and values (formalized in the Moral Foundation Theory). This study begins by mapping the ecology of current emotional AI use in various contexts such as workplace, education, healthcare, personal assistance, etc. It then provides a brief review and critique of current applications of the TAM and the Moral Foundation Theory in studying how humans judge smart technologies. Finally, we propose the Three-pronged Analytical Framework, offering recommendations on how future studies of technological acceptance could be conducted from the questionnaire design to building statistical models