  Recent work on machine learning has begun to consider issues of fairness. In
this paper, we extend the concept of fairness to recommendation. In particular,
we show that in some recommendation contexts, fairness may be a multisided
concept, in which fair outcomes for multiple individuals need to be considered.
Based on these considerations, we present a taxonomy of classes of
fairness-aware recommender systems and suggest possible fairness-aware
recommendation architectures.
