Folk theories of algorithmic recommendations on Spotify: Enacting data assemblages in the global South

Big Data and Society 7 (1) (2020)
  Copy   BIBTEX

Abstract

This paper examines folk theories of algorithmic recommendations on Spotify in order to make visible the cultural specificities of data assemblages in the global South. The study was conducted in Costa Rica and draws on triangulated data from 30 interviews, 4 focus groups with 22 users, and the study of “rich pictures” made by individuals to graphically represent their understanding of algorithmic recommendations. We found two main folk theories: one that personifies Spotify and another one that envisions it as a system full of resources. Whereas the first theory emphasizes local conceptions of social relations to make sense of algorithms, the second one stresses the role of algorithms in providing a global experience of music and technology. We analyze why people espouse either one of these theories and how these theories provide users with resources to enact different modalities of power and resistance in relation to recommendation algorithms. We argue that folk theories thus offer a productive way to broaden understanding of what agency means in relation to algorithms.

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 91,438

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Similar books and articles

Democratizing Algorithmic Fairness.Pak-Hang Wong - 2020 - Philosophy and Technology 33 (2):225-244.
Algorithmic randomness in empirical data.James W. McAllister - 2003 - Studies in History and Philosophy of Science Part A 34 (3):633-646.
Algorithmic randomness in empirical data.James W. McAllister - 2003 - Studies in History and Philosophy of Science Part A 34 (3):633-646.
Algorithmic Music and the Philosophy of Time.Julian Rohrhuber - 2018 - In Alex McLean & Roger T. Dean (eds.), The Oxford Handbook of Algorithmic Music. Oxford University Press.

Analytics

Added to PP
2020-11-24

Downloads
28 (#560,541)

6 months
13 (#186,332)

Historical graph of downloads
How can I increase my downloads?