NICK SEAVER

I’m an anthropologist who studies how people use technology to make sense of cultural things.

I teach in the Department of Anthropology at Tufts University, where I also direct the program in Science, Technology, and Society.

My first book is about the people who make music recommender systems and how they think about their work. It’s called Computing Taste: Algorithms and the Makers of Music Recommendation, and you can pre-order it from the University of Chicago Press.

I’m currently studying the rise of attention as a value and virtue in machine learning worlds, from the new tech humanism to the infrastructure of neural networks.

Below, you can find links to my publications. If you’d like to read anything here and can’t access it, please feel free to email me for a copy.
I’m an anthropologist who studies how people use technology to make sense of cultural things.

I teach in the Department of Anthropology at Tufts University, where I also direct the program in Science, Technology, and Society.

My first book is about the people who make music recommender systems and how they think about their work. It’s called Computing Taste: Algorithms and the Makers of Music Recommendation, and you can pre-order it from the University of Chicago Press.

I’m currently studying the rise of attention as a value and virtue in machine learning worlds, from the new tech humanism to the infrastructure of neural networks.

Below, you can find links to my publications. If you’d like to read anything here and can’t access it, please feel free to email me for a copy.

For the people who make recommender systems, the fact that care and scale seem intrinsically opposed is a problem. This article describes how they try to solve it. They do so not by giving up on care or abandoning their desire to scale, but by reimagining the terms of their relationship—redefining what care and scale mean in the process.
August 2021

Everything Lies in a Space: Cultural Data and Spatial Reality. Journal of the Royal Anthropological Institute 27 (S1): 43–61.
This essay examines the use of spatializing techniques for analyzing cultural data in music recommendation and post-war cognitive anthropology. It explores three similarities between these fields: How spatial analyses engender a sense of continuous, enveloping milieu from discrete and often sparse data; how spatialization is used to grant culture a kind of reality rooted in pragmatic action and scientific quantification; and how spatial representations of culture are essentially anticipatory for the people who make them, transforming the near future into the nearby.
April 2021

Towards an Anthropology of Data. 2021 Special Issue of the Journal of the Royal Anthropological Institute.

Co-edited with Rachel Douglas-Jones and Tone Walford.
This special issue draws data’s apparent novelty into conversation with many classic anthropological concepts, from kinship to value to personhood. The work collected here attends to how discourses, practices, and imaginaries of data are reconfiguring familiar domains in unfamiliar ways.
April 2021

How do the developers of music recommender systems think about the composition of their teams, the differences between themselves and their users, and the resulting technical consequences? People who work on these systems are generally reluctant to recognize demographic categories as technically salient. Instead, they have come to understand the difference between themselves and their users primarily in terms of musical enthusiasm, or avidity.
March 2021

Captivating Algorithms: Recommender Systems as Traps. Journal of Material Culture 24 (4): 421–436.
This article explores a tendency among the makers of recommender systems to describe their purpose as “hooking” people—enticing them into frequent or enduring usage.  Anthropological theories about animal trapping prove useful for thinking about how these systems embody models of their users and sit within broader infrastructural ecologies of knowledge and technology.
December 2019


Revised July 2022 in Somerville, MA