Informing Algorithmic Literacy Through User Folk Theories

Abstract

As part of a broader information literacy agenda, academic libraries are interested in advancing algorithmic literacy. Folk theories of algorithmic decision-making systems, such as recommender systems, can provide insights into designing and delivering enhanced algorithmic literacy initiatives. Users of the Spotify music recommendation systems were surveyed and interviewed to elicit their folk theories about how music recommendations are made. Seven folk theories emerged from this study and are grouped into four themes: agency, context, trust, and feelings. These four themes are used to illustrate how folk theories can inform algorithmic literacy programming and curricula.