Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning

TL;DR

This paper evaluates fairness issues in deep metric learning (DML), revealing biases in representations trained on imbalanced data and proposing a method to mitigate subgroup performance gaps.

Contribution

It introduces a new fairness benchmark for DML, analyzes bias propagation in downstream tasks, and proposes PARADE to reduce subgroup disparities.

Findings

Bias in DML propagates to downstream tasks.

Re-balancing data alone does not eliminate bias.

PARADE reduces subgroup performance gaps.

Abstract

Deep metric learning (DML) enables learning with less supervision through its emphasis on the similarity structure of representations. There has been much work on improving generalization of DML in settings like zero-shot retrieval, but little is known about its implications for fairness. In this paper, we are the first to evaluate state-of-the-art DML methods trained on imbalanced data, and to show the negative impact these representations have on minority subgroup performance when used for downstream tasks. In this work, we first define fairness in DML through an analysis of three properties of the representation space -- inter-class alignment, intra-class alignment, and uniformity -- and propose finDML, the fairness in non-balanced DML benchmark to characterize representation fairness. Utilizing finDML, we find bias in DML representations to propagate to common downstream classification tasks. Surprisingly, this bias is propagated even when training data in the downstream task is re-balanced. To address this problem, we present Partial Attribute De-correlation (PARADE) to de-correlate feature representations from sensitive attributes and reduce performance gaps between subgroups in both embedding space and downstream metrics.