  With the growing interest in the machine learning community to solve
real-world problems, it has become crucial to uncover the hidden reasoning
behind their decisions by focusing on the fairness and auditing the predictions
made by these black-box models. In this paper, we propose a novel method to
address two key issues: (a) Can we simultaneously learn fair disentangled
representations while ensuring the utility of the learned representation for
downstream tasks, and (b)Can we provide theoretical insights into when the
proposed approach will be both fair and accurate. To address the former, we
propose the method FRIED, Fair Representation learning using Interpolation
Enabled Disentanglement. In our architecture, by imposing a critic-based
adversarial framework, we enforce the interpolated points in the latent space
to be more realistic. This helps in capturing the data manifold effectively and
enhances the utility of the learned representation for downstream prediction
tasks. We address the latter question by developing a theory on
fairness-accuracy trade-offs using classifier-based conditional mutual
information estimation. We demonstrate the effectiveness of FRIED on datasets
of different modalities - tabular, text, and image datasets. We observe that
the representations learned by FRIED are overall fairer in comparison to
existing baselines and also accurate for downstream prediction tasks.
Additionally, we evaluate FRIED on a real-world healthcare claims dataset where
we conduct an expert aided model auditing study providing useful insights into
opioid ad-diction patterns.
