MAPPING VALUE(S) IN AI: THE CASE OF YOUTUBE
Keywords:mutidisciplinary methodology, recommender systems, YouTube
This paper presents a multidisciplinary approach (media studies, computer science, and legal scholarship) for the analysis of systems that rely on AI components as central components of their design. Taking recommender systems more generally and the one built by YouTube more specifically, we develop a methodology for conceptualizing and studying the broad array of “ideas”, “norms”, or “values” such systems mobilize. Instead of limiting ourselves to a narrow understanding of these terms, we take into account, for example, translations from economic models, social theories, legal requirements, ethical principles, technical knowledge, experiential evaluations, or other constructs used to define and justify design goals and decisions that shape the production of technical objects and, consequently, the object themselves. In this paper we discuss three directions for analysis and present first results. Investigating technical knowledge includes the study of scholarly literature and experimentation with concrete objects such as LensKit for Python to understand the “ambient” knowledge and normativity engineers and designers draw on. Investigating local circumstances involves ethnographic analysis, but also the reconstruction of the business models and legal environment that weigh on YouTube’s design. Analyzing a system in use can draw on technical observation, via scraping or API data, of the actual dynamics of recommendation that emerge when users enter into the equation. Taken together, these three approaches can “encircle” the various moments where value(s) are shaped and put into work in the context of systems where direct access to specifications is improbable.