  Ethical AI spans a gamut of considerations. Among these, the most popular
ones, fairness and interpretability, have remained largely distinct in
technical pursuits. We discuss and elucidate the differences between fairness
and interpretability across a variety of dimensions. Further, we develop two
principles-based frameworks towards developing ethical AI for the future that
embrace aspects of both fairness and interpretability. First, interpretability
for fairness proposes instantiating interpretability within the realm of
fairness to develop a new breed of ethical AI. Second, fairness and
interpretability initiates deliberations on bringing the best aspects of both
together. We hope that these two frameworks will contribute to intensifying
scholarly discussions on new frontiers of ethical AI that brings together
fairness and interpretability.
