Exploring Folk Theories Of Algorithmic News Curation For Explainable Design

Algorithmic news curation determines users' news exposure in the online environment. Despite its usefulness, it also comes along with the problem of algorithmic opacity. To combat this, explainable algorithmic news curation systems are necessary. One user-centered solution to design these systems can be achieved through the systematic exploration of user folk theories. For this, we conducted twelve in-depth semi-structured interviews to explore (1) the user preferences for explainable system design, and (2) folk theories of algorithmic news curation. By applying qualitative content analysis, we found a psychological trade-off between the desire for transparency and feelings of creepiness, thus a preference for explanations to be hidden. Furthermore, we identified eight assumptions of folk theories. The results are compared to previous folk theories and discussed in terms of the 'sweet spot' of system transparency. We conclude that exploring folk theories is a key requirement for designing explainable algorithmic news curation systems.