By looking at the politics of classification within machine learning systems, this article demonstrates why the automated interpretation of images is an inherently social and political project. We begin by asking what work images do in computer vision systems, and what is meant by the claim that computers can “recognize” an image? Next, we look at the method for introducing images into computer systems and look at how taxonomies order the foundational concepts that will determine how a system interprets the (...) world. Then we turn to the question of labeling: how humans tell computers which words will relate to a given image. What is at stake in the way AI systems use these labels to classify humans, including by race, gender, emotions, ability, sexuality, and personality? Finally, we turn to the purposes that computer vision is meant to serve in our society—the judgments, choices, and consequences of providing computers with these capacities. Methodologically, we call this an archeology of datasets: studying the material layers of training images and labels, cataloguing the principles and values by which taxonomies are constructed, and analyzing how these taxonomies create the parameters of intelligibility for an AI system. By doing this, we can critically engage with the underlying politics and values of a system, and analyze which normative patterns of life are assumed, supported, and reproduced. (shrink)

How can one ‘see’ the operationalization of contemporary visual culture, given the imperceptibility and apparent automation of so many processes and dimensions of visuality? Seeing – as a position from a singular mode of observation – has become problematic since many visual elements, techniques, and forms of observing are highly distributed through data practices of collection, analysis and prediction. Such practices are subtended by visual cultural techniques that are grounded in the development of image collections, image formatting and hardware design. (...) In this article, we analyze recent transformations in forms of prediction and data analytics associated with spectacular performances of computation. We analyze how transformations in the collection and accumulation of images as ensembles by platforms have a qualitative and material effect on the emergent sociotechnicality of platform ‘life’ and ‘perception’. Reconstructing the visual transformations that allow artificial intelligence assemblages to operate allows some sense of their heteronomous materiality and contingency. (shrink)

Media philosopher Vilém Flusser proposed a revolutionary new way of thinking about photography.

In this highly topical book, Agamben ultimately arrives at original ideas about the future of democracy and casts a new light on the hidden relationship that ties law to violence.

In Contagious Architecture, Luciana Parisi offers a philosophicalinquiry into the status of the algorithm in architectural and interaction design.

This article responds to recent debates in critical algorithm studies about the significance of the term “algorithm.” Where some have suggested that critical scholars should align their use of the term with its common definition in professional computer science, I argue that we should instead approach algorithms as “multiples”—unstable objects that are enacted through the varied practices that people use to engage with them, including the practices of “outsider” researchers. This approach builds on the work of Laura Devendorf, Elizabeth Goodman, (...) and Annemarie Mol. Different ways of enacting algorithms foreground certain issues while occluding others: computer scientists enact algorithms as conceptual objects indifferent to implementation details, while calls for accountability enact algorithms as closed boxes to be opened. I propose that critical researchers might seek to enact algorithms ethnographically, seeing them as heterogeneous and diffuse sociotechnical systems, rather than rigidly constrained and procedural formulas. To do so, I suggest thinking of algorithms not “in” culture, as the event occasioning this essay was titled, but “as” culture: part of broad patterns of meaning and practice that can be engaged with empirically. I offer a set of practical tactics for the ethnographic enactment of algorithmic systems, which do not depend on pinning down a singular “algorithm” or achieving “access,” but which rather work from the partial and mobile position of an outsider. (shrink)

This was the first cross-over book into the history of science written by an historian of economics. It shows how 'history of technology' can be integrated with the history of economic ideas. The analysis combines Cold War history with the history of postwar economics in America and later elsewhere, revealing that the Pax Americana had much to do with abstruse and formal doctrines such as linear programming and game theory. It links the literature on 'cyborg' to economics, an element missing (...) in literature to date. The treatment further calls into question the idea that economics has been immune to postmodern currents, arguing that neoclassical economics has participated in the deconstruction of the integral 'self'. Finally, it argues for an alliance of computational and institutional themes, and challenges the widespread impression that there is nothing else besides American neoclassical economic theory left standing after the demise of Marxism. (shrink)

Prehensity -- Intensity -- Potentiality -- Sensibility.

In a 1955 lecture the physicist Richard Feynman reflected on the place of doubt within scientific practice. ‘Permit us to question, to doubt, to not be sure’, proposed Feynman, ‘it is possible to live and not to know’. In our contemporary world, the science of machine learning algorithms appears to transform the relations between science, knowledge and doubt, to make even the most doubtful event amenable to action. What might it mean to ‘leave room for doubt’ or ‘to live and (...) not to know’ in our contemporary culture, where the algorithm plays a major role in the calculability of doubts? I propose a posthuman mode of doubt that decentres the liberal humanist subject. In the science of machine learning algorithms the doubts of human and technological beings nonetheless dwell together, opening onto a future that is never fully reduced to the single output signal, to the optimised target. (shrink)