Seeing like an infrastructure: avidity and difference in algorithmic recommendation. Issue 4 (3rd September 2021)

• Record Type: Journal Article
• Authors: Seaver, Nick
• Is Part Of: Cultural studies. Volume 35:Issue 4/5(2021)
• Page Start: 771
• Page End: 791
• Publication Date: 2021-09-03
• Subjects: Recommender systems -- avidity -- algorithms -- infrastructure -- music; Culture -- Periodicals; Popular culture -- Periodicals; 306
• DOI: 10.1080/09502386.2021.1895248
• Languages: English
• ISSNs: 0950-2386

Abstract

ABSTRACT: As the influence of algorithmic systems has grown, critics have come to appreciate that algorithms are not autonomous technical forces, but rather heterogeneous sociotechnical systems. The people who build and maintain these infrastructures play integral roles in their functioning: in the tight and continuous cycles of contemporary software development, the thinking of developers shapes how data drives 'data-driven' organizations. This article contributes to contemporary debates on infrastructural politics by describing how the vernacular social theorizing of one group of developers tangles with their technical work. Drawing on ethnographic fieldwork with developers of music recommender systems in the US, I examine how they understand the variability of music listeners. I find that the dominant frame for making sense of listener variation is avidity : a level of enthusiasm for music, which manifests as a willingness to expend effort in finding listening material. For people working in this industry, avidity displaces other ways of understanding human variety – particularly demography. While the technical communities behind these systems were predominantly white and male, they understood the difference that set them apart from most users to be their enthusiasm for music. Centreing avidity provided a way to claim elite cultural status and to avoid talking about demographic diversity. It also reflects the infrastructures through which recommender system developers know and intervene upon their users: avidity is what users look like when seen through interaction logs. Less avid users leave fewer traces, and the goal of the recommender system is to encourage them to leave more. As a result, the figure of the less avid listener serves to justify increasingly rapacious data collection practices.