  In machine learning, disparity metrics are often defined by measuring the
difference in the performance or outcome of a model, across different
sub-populations (groups) of datapoints. Thus, the inputs to disparity
quantification consist of a model's predictions $\hat{y}$, the ground-truth
labels for the predictions $y$, and group labels $g$ for the data points.
Performance of the model for each group is calculated by comparing $\hat{y}$
and $y$ for the datapoints within a specific group, and as a result, disparity
of performance across the different groups can be calculated. In many real
world scenarios however, group labels ($g$) may not be available at scale
during training and validation time, or collecting them might not be feasible
or desirable as they could often be sensitive information. As a result,
evaluating disparity metrics across categorical groups would not be feasible.
On the other hand, in many scenarios noisy groupings may be obtainable using
some form of a proxy, which would allow measuring disparity metrics across
sub-populations. Here we explore performing such analysis on computer vision
models trained on human faces, and on tasks such as face attribute prediction
and affect estimation. Our experiments indicate that embeddings resulting from
an off-the-shelf face recognition model, could meaningfully serve as a proxy
for such estimation.
