The recent interest in Big Data has generated a broad range of new academic, corporate, and policy practices along with an evolving debate amongst its proponents, detractors, and skeptics. While the practices draw on a common set of tools, techniques, and technologies, most contributions to the debate come either from a particular disciplinary perspective or with an eye on a domain-specific issue. A close examination of these contributions reveals a set of common problematics that arise in various guises in different places. It also demonstrates the need for a critical synthesis of the conceptual and practical dilemmas surrounding Big Data. The purpose of this article is to provide such a synthesis by drawing on relevant writings in the sciences, humanities, policy, and trade literature. In bringing these diverse literatures together, we aim to shed light on the common underlying issues that concern and affect all of these areas. By contextualizing the phenomenon of Big Data within larger socio-economic developments, we also seek to provide a broader understanding of its drivers, barriers, and challenges. This approach allows us to identify attributes of Big Data that need to receive more attentionautonomy, opacity, and generativity, disparity, and futurity -leading to questions and ideas for moving beyond dilemmas.

A rigorous theory of money, credit, and bankruptcy in the context of a mixed economy, uniting Walrasian general equilibrium with macroeconomic dynamics and Schumpeterian innovation.

Digital artifacts have novel properties that largely derive from the processes that mediate their creation, and that can be best understood by a close examination of such processes. This paper introduces the concept of "quasiobject" to characterize these objects and elucidate the activities that comprise their mediations. A case study of "bugs" is analyzed to illustrate exemplary activities of justification, qualification, and binding in the process of bug fixing in Free/Open Source Software development. The findings of the case study lead to broader reflections on the character of digital artifacts in general. The relationship of "quasi-object" to other similar concepts are explored.

The division of labor between humans and computer systems has changed along both technical and human dimensions. Technically, there has been a shift from technologies of automation, the aim of which was to disallow human intervention at nearly all points in the system, to technologies of “heteromation” that push critical tasks to end users as indispensable mediators. As this has happened, the large population of human beings who have been driven out by the first type of technology are drawn back into the computational fold by the second type. Turning artificial intelligence on its head, one technology fills the gap created by the other, but with a vengeance that unsettles established mechanisms of reward, fulfillment, and compensation. In this fashion, replacement of human beings and their irrelevance to technological systems has given way to new “modes of engagement” with remarkable social, economic, and ethical implications. In this paper we provide a historical backdrop for heteromation and explore and explicate some of these displacements through analysis of a number of cases, including Mechanical Turk, the video games FoldIt and League of Legends, and social media.

This book is a critique of Artificial Intelligence (AI) from the perspective of cognitive science – it seeks to examine what we have learned about human cognition from AI successes and failures. The book's goal is to separate those 'AI dreams' that either have been or could be realized from those that are constructed through discourse and are unrealizable. AI research has advanced many areas that are intellectually compelling and holds great promise for advances in science, engineering, and practical systems. After the 1980s, however, the field has often struggled to deliver widely on these promises. This book breaks new ground by analyzing how some of the driving dreams of people practicing AI research become valued contributions, while others devolve into unrealized and unrealizable projects.