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.

This article responds to debates in critical algorithm studies about the significance of the term “algorithm,” arguing that we should approahc algorithms as “multiples”—unstable objects that are enacted through the varied practices that people use to engage with them, including the practices of “outsider” researchers. Different ways of enacting algorithms foreground certain issues while occluding others. I propose that critical researchers might seek to enact algorithms ethnographically, seeing them as heterogeneous and diffuse sociotechnical systems, rather than rigidly constrained and procedural formulas.
November 2017


Revised July 2022 in Somerville, MA