HyperFair: A Soft Approach to Integrating Fairness Criteria

TL;DR

HyperFair introduces a flexible framework for incorporating soft fairness constraints into recommender systems using probabilistic soft logic, enhancing fairness without significantly compromising accuracy.

Contribution

It presents a novel, interpretable method for enforcing fairness in hybrid recommenders via regularization and retrofitting, applicable to black-box models.

Findings

HyperFair effectively improves fairness in recommendations.

The approach maintains competitive prediction accuracy.

It offers a versatile tool for fairness in diverse recommender systems.

Abstract

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.