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Computing Taste
"For the people who make them, music recommender systems hold a utopian promise: they can broaden listeners' horizons and help obscure musicians find audiences, taking advantage of the enormous catalogs offered by companies like Spotify, Apple Music, and their kin. But for critics, recommender systems have come to epitomize the potential harms of algorithms: they seem to reduce expressive culture to numbers, they normalize ever-broadening data collection, and they profile their users for commercial ends, tearing the social fabric into isolated patches of atomized individuals. Drawing on years of ethnographic fieldwork, anthropologist Nick Seaver offers an account of how the makers of music r...
digitalSTS
New perspectives on digital scholarship that speak to today's computational realities Scholars across the humanities, social sciences, and information sciences are grappling with how best to study virtual environments, use computational tools in their research, and engage audiences with their results. Classic work in science and technology studies (STS) has played a central role in how these fields analyze digital technologies, but many of its key examples do not speak to today’s computational realities. This groundbreaking collection brings together a world-class group of contributors to refresh the canon for contemporary digital scholarship. In twenty-five pioneering and incisive essays,...
The Cambridge Companion to Music in Digital Culture
Digital technology has profoundly transformed almost all aspects of musical culture. This book explains how and why.
Towards an Anthropology of Data
This volume presents a set of theoretically inventive pieces that engage with data across its many locations, from government databases to ecological field stations, from kitchen tables to concrete bunkers. Contributors demonstrate how thinking with data can be conceptually generative for anthropology, prompting us to reconsider our understanding of topics including bodies, persons, and the social itself Shows how 'big' data which may have once seemed limited to business or high tech, ethnographers are now finding data – and its attendant values and practices – in their field sites around the world Examines how data has motivated a sweep of dystopian visions, signaling the invasion of privacy, political manipulation, or shadowy data doubles Discusses how anthropologists have been cautious in taking data itself as an object of theoretical interest, even as the effects of data become manifest in our ethnographies By putting data in its place, the chapters collected here develop conceptual tools that will prove useful for anthropologists who find 'data' in their data
Predict and Surveil
Predict and Surveil offers an unprecedented, inside look at how police use big data and new surveillance technologies. Sarah Brayne conducted years of fieldwork with the LAPD--one of the largest and most technically advanced law enforcement agencies in the world-to reveal the unmet promises and very real perils of police use of data--driven surveillance and analytics.
Data, Now Bigger and Better!
Data is too big to be left to the data analysts. Data: Now Bigger and Better brings together researchers whose work is deeply informed by the conceptual frameworks of anthropologyframeworks that are comparative as well as field-based. From kinship to gifts, everything old becomes rich with new insight when the anthropological archive washes over big data. Bringing together anthropology s classic debates and contemporary interventions, the book counters the future-oriented speculation so characteristic of discussions regarding big data. Drawing on the long-standing experience in industry contexts, the contributors also provide analytical provocations that can help reframe some of the most important shifts in technology and society in the first half of the twenty-first century."
Algorithms of Anxiety
Machine learning algorithms are widely presumed to herald a world in which the crippling burdens of anxiety can be left behind. The digital revolution promises a brave new world where individuals, communities and organizations can at last take control of the future – anticipating, designing and commanding the future, possibly even with mathematical exactitude. Yet, paradoxically, algorithms have unleashed widespread fears and forebodings about the impact of digital technologies. Whether it’s worries about unemployment, distress about social media’s harmful effects on teenagers, or the fear of intrusive digital surveillance, we live in an age of turbo-charged anxiety where the prophecie...
Spotify Teardown
An innovative investigation of the inner workings of Spotify that traces the transformation of audio files into streamed experience. Spotify provides a streaming service that has been welcomed as disrupting the world of music. Yet such disruption always comes at a price. Spotify Teardown contests the tired claim that digital culture thrives on disruption. Borrowing the notion of “teardown” from reverse-engineering processes, in this book a team of five researchers have playfully disassembled Spotify's product and the way it is commonly understood. Spotify has been hailed as the solution to illicit downloading, but it began as a partly illicit enterprise that grew out of the Swedish file-...
Free Listening
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Algorithms
Algorithms: Technology, Culture, Politics develops a relational, situated approach to algorithms. It takes a middle ground between theories that give the algorithm a singular and stable meaning in using it as a central analytic category for contemporary society and theories that dissolve the term into the details of empirical studies. The book discusses algorithms in relation to hardware and material conditions, code, data, and subjects such as users, programmers, but also “data doubles”. The individual chapters bridge critical discussions on bias, exclusion, or responsibility with the necessary detail on the contemporary state of information technology. The examples include state-of-the-art applications of machine learning, such as self-driving cars, and large language models such as GPT. The book will be of interest for everyone engaging critically with algorithms, particularly in the social sciences, media studies, STS, political theory, or philosophy. With its broad scope it can serve as a high-level introduction that picks up and builds on more than two decades of critical research on algorithms.
