  Recommender systems are being employed across an increasingly diverse set of
domains that can potentially make a significant social and individual impact.
For this reason, considering fairness is a critical step in the design and
evaluation of such systems. In this paper, we introduce HyperFair, a general
framework for enforcing soft fairness constraints in a hybrid recommender
system. HyperFair models integrate variations of fairness metrics as a
regularization of a joint inference objective function. We implement our
approach using probabilistic soft logic and show that it is particularly
well-suited for this task as it is expressive and structural constraints can be
added to the system in a concise and interpretable manner. We propose two ways
to employ the methods we introduce: first as an extension of a probabilistic
soft logic recommender system template; second as a fair retrofitting technique
that can be used to improve the fairness of predictions from a black-box model.
We empirically validate our approach by implementing multiple HyperFair hybrid
recommenders and compare them to a state-of-the-art fair recommender. We also
run experiments showing the effectiveness of our methods for the task of
retrofitting a black-box model and the trade-off between the amount of fairness
enforced and the prediction performance.
