Multiperspective and Multidisciplinary Treatment of Fairness in Recommender Systems Research

Abstract

In the communities of UMAP, RecSys, and similar venues, fairness of recommender systems has primarily been addressed from the perspective of computer science and artificial intelligence, e.g., by devising computational bias and fairness metrics or elaborating debiasing algorithms. In contrast, we advocate taking a multiperspective and multidisciplinary viewpoint to complement this technical perspective. This involves considering the variety of stakeholders in the value chain of recommender systems as well as interweaving expertise from various disciplines, in particular, computer science, law, ethics, sociology, and psychology (e.g., studying discrepancies between computational metrics of bias and fairness and their actual human perception, and considering the legal and regulatory context recommender systems are embedded in).