Compositional Fairness Constraints for Graph Embeddings

TL;DR

This paper introduces a flexible adversarial framework for enforcing multiple fairness constraints on graph embeddings, enabling customizable fairness in applications like social recommendations.

Contribution

It presents a novel compositional adversarial approach that allows combining different fairness constraints during inference on graph embeddings.

Findings

Framework effectively enforces fairness constraints in experiments

Allows multiple fairness constraints to be combined flexibly

Improves fairness without sacrificing embedding quality

Abstract

Learning high-quality node embeddings is a key building block for machine learning models that operate on graph data, such as social networks and recommender systems. However, existing graph embedding techniques are unable to cope with fairness constraints, e.g., ensuring that the learned representations do not correlate with certain attributes, such as age or gender. Here, we introduce an adversarial framework to enforce fairness constraints on graph embeddings. Our approach is compositional---meaning that it can flexibly accommodate different combinations of fairness constraints during inference. For instance, in the context of social recommendations, our framework would allow one user to request that their recommendations are invariant to both their age and gender, while also allowing another user to request invariance to just their age. Experiments on standard knowledge graph and recommender system benchmarks highlight the utility of our proposed framework.