HyperFair: A Soft Approach to Integrating Fairness Criteria
This work addresses fairness in recommender systems, which is critical for social and individual impact, though it appears incremental as it builds on existing methods like probabilistic soft logic.
The paper tackles the problem of enforcing fairness in hybrid recommender systems by introducing HyperFair, a framework that integrates fairness metrics as regularization, and demonstrates its effectiveness through empirical validation against a state-of-the-art fair recommender.
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.