Multisided Fairness for Recommendation
This work addresses fairness issues in recommendation systems, which is an incremental extension of existing fairness concepts to a new domain.
The paper tackles the problem of extending fairness concepts to recommendation systems, proposing that fairness can be multisided to consider outcomes for multiple individuals, and it presents a taxonomy and architectures for fairness-aware recommender systems.
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.