  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.
