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.

The Political Economy of Attention. Annual Review of Anthropology 50: 309–325.

Written with Morten Axel Pedersen and Kristoffer Albris.
While attention has rarely been an explicit focus of anthropological inquiry, it has still played an important—if mostly tacit—part in many anthropological debates. In this essay, we review potential avenues for an incipient anthropology of attention, which studies how attentional technologies and techniques mold human minds and bodies.
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 Antonia 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

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

What Should an Anthropology of Algorithms Do? Cultural Anthropology 33 (3): 375–385.
When encountering algorithmic systems, anthropologists often revert to a defense of the human, pitted against these technologies. This short essay argues against embracing the “analog slot,” suggesting that we should problematize the presumed opposition between humans and algorithms by attending to the humans involved in making algorithmic systems work.
August 2018


Revised July 2022 in Somerville, MA