Improving Group Fairness in Tensor Completion via Imbalance Mitigating Entity Augmentation

TL;DR

This paper introduces STAFF, a method that augments tensors with additional entities to improve group fairness in tensor completion, achieving better fairness and accuracy trade-offs.

Contribution

The paper proposes a novel augmentation technique to mitigate group bias in tensor completion, balancing fairness and performance.

Findings

STAFF achieves up to 36% lower MSE than baselines.

STAFF reduces group bias while maintaining low tensor completion error.

Consistently outperforms existing methods in fairness-accuracy trade-offs.

Abstract

Group fairness is important to consider in tensor decomposition to prevent discrimination based on social grounds such as gender or age. Although few works have studied group fairness in tensor decomposition, they suffer from performance degradation. To address this, we propose STAFF(Sparse Tensor Augmentation For Fairness) to improve group fairness by minimizing the gap in completion errors of different groups while reducing the overall tensor completion error. Our main idea is to augment a tensor with augmented entities including sufficient observed entries to mitigate imbalance and group bias in the sparse tensor. We evaluate \method on tensor completion with various datasets under conventional and deep learning-based tensor models. STAFF consistently shows the best trade-off between completion error and group fairness; at most, it yields 36% lower MSE and 59% lower MADE than the second-best baseline.