A Public Service Media Perspective on the Algorithmic Amplification of Cultural Content

Streaming entertainment platforms curate cultural content such as music, film, and literature, significantly influencing the nature of individual cultural experience. Recommender systems play an important role in this process, using algorithms optimized for factors such as engagement, retention, and revenue to guide curatorial decisions. In this context, multiple studies have demonstrated that recommender systems amplify some genres or groups of content creators while overlooking others. Although these studies highlight distortions in the content people consume, they do not provide guidance on what appropriate curation of cultural content should entail. To address this lack, we analyze algorithmic amplification in the specific context of curation of cultural content. We focus on disparities between personalization, a goal of current recommender systems, and normative concerns about the algorithmic curation of cultural content. Specifically, we explore how curation can be developed in order to promote cultural experiences oriented toward social justice and the public good. For guidance on such normative concerns, we turn to principles underlying public service media (PSM) systems in democratic societies. These principles, refined over decades in the programming of cultural content, expand the desiderata of recommender systems—both commercial and noncommercial—to include values furthering the democratic well-being and the cultural and social development of contemporary societies. Building on our recent work developing a metric to measure two PSM principles, commonality and diversity, in recommender systems, and with a focus on music recommendation, we propose a more comprehensive research program toward incorporating such principles into the design of recommender systems for cultural content, inviting the research community to address how such normative goals could transform processes of algorithmic amplification.