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.

Knowing Algorithms. In digitalSTS: A Field Guide for Science & Technology Studies, edited by Janet Vertesi and David Ribes. Princeton: Princeton University Press, 412–422.
This chapter describes some of the challenges facing people who want to know things about algorithms, especially “outsiders.” It argues that many of these challenges stem from an overly narrow understanding of what an “algorithm” is, which leads critics to imagine them as simple processes that only need to be revealed to be known. Against this revelatory model, the chapter advances an understanding of algorithms as “algorithmic systems,” heterogeneously composed of computational processes, data flows, people, and a host of infrastructural supports.
May 2019


Revised July 2022 in Somerville, MA