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.

摘要

随着算法系统的影响力越来越大，批评家们开始意识到算法不是自主的技术力量，而是异构的社会技术系统。构建和维护这些基础设施的人员在其功能中发挥着不可或缺的作用：在当代软件开发的紧密和连续循环中，开发人员的思维塑造了数据驱动“数据驱动”组织的方式。本文通过描述一组开发人员的本土社会理论如何与他们的技术工作纠缠在一起，为当代关于基础设施政治的辩论做出了贡献。通过与美国音乐推荐系统开发人员的民族志实地考察，我研究了他们如何理解音乐听众的可变性。我发现理解听众变化的主要框架是热情：对音乐的某种程度的热情，表现为愿意花费精力寻找聆听材料。对于在这个行业工作的人来说，热情取代了理解人类多样性的其他方式——尤其是人口统计学。虽然这些系统背后的技术社区主要是白人和男性，但他们明白将他们与大多数用户区分开来的区别在于他们对音乐的热情。中心化的狂热提供了一种声称精英文化地位和避免谈论人口多样性的方法。它还反映了推荐系统开发人员了解和干预用户的基础设施：热情是用户通过交互日志看到的样子。不太狂热的用户留下的痕迹较少，推荐系统的目标是鼓励他们留下更多的痕迹。其结果，