Bonnie A. Nardi

Hamid R. Ekbia

Hamid Reza Ekbia

An exploration of a new division of labor between machines and humans, in which people provide value to the economy with little or no compensation. The computerization of the economy—and everyday life—has transformed the division of labor between humans and machines, shifting many people into work that is hidden, poorly compensated, or accepted as part of being a “user” of digital technology. Through our clicks and swipes, logins and profiles, emails and posts, we are, more or less willingly, participating in digital activities that yield economic value to others but little or no return to us. Hamid Ekbia and Bonnie Nardi call this kind of participation—the extraction of economic value from low-cost or free labor in computer-mediated networks—“heteromation.” In this book, they explore the social and technological processes through which economic value is extracted from digitally mediated work, the nature of the value created, and what prompts people to participate in the process. Arguing that heteromation is a new logic of capital accumulation, Ekbia and Nardi consider different kinds of heteromated labor: communicative labor, seen in user-generated content on social media; cognitive labor, including microwork and self-service; creative labor, from gaming environments to literary productions; emotional labor, often hidden within paid jobs; and organizing labor, made up of collaborative groups such as citizen scientists. Ekbia and Nardi then offer a utopian vision: heteromation refigured to bring end users more fully into the prosperity of capitalism.

Heteromation, and Other Stories of Computing and Capitalism

Cassidy R. Sugimoto

Michael Mattioli

Big Data Is Not a Monolith

Perspectives on the varied challenges posed by big data for health, science, law, commerce, and politics. Big data is ubiquitous but heterogeneous. Big data can be used to tally clicks and traffic on web pages, find patterns in stock trades, track consumer preferences, identify linguistic correlations in large corpuses of texts. This book examines big data not as an undifferentiated whole but contextually, investigating the varied challenges posed by big data for health, science, law, commerce, and politics. Taken together, the chapters reveal a complex set of problems, practices, and policies. The advent of big data methodologies has challenged the theory-driven approach to scientific knowledge in favor of a data-driven one. Social media platforms and self-tracking tools change the way we see ourselves and others. The collection of data by corporations and government threatens privacy while promoting transparency. Meanwhile, politicians, policy makers, and ethicists are ill-prepared to deal with big data's ramifications. The contributors look at big data's effect on individuals as it exerts social control through monitoring, mining, and manipulation; big data and society, examining both its empowering and its constraining effects; big data and science, considering issues of data governance, provenance, reuse, and trust; and big data and organizations, discussing data responsibility, “data harm,” and decision making. Contributors Ryan Abbott, Cristina Alaimo, Kent R. Anderson, Mark Andrejevic, Diane E. Bailey, Mike Bailey, Mark Burdon, Fred H. Cate, Jorge L. Contreras, Simon DeDeo, Hamid R. Ekbia, Allison Goodwell, Jannis Kallinikos, Inna Kouper, M. Lynne Markus, Michael Mattioli, Paul Ohm, Scott Peppet, Beth Plale, Jason Portenoy, Julie Rennecker, Katie Shilton, Dan Sholler, Cassidy R. Sugimoto, Isuru Suriarachchi, Jevin D. West

Hamid R. Ekbia

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