Monumental advances in deep learning have led to unprecedented achievements
across a multitude of domains. While the performance of deep neural networks is
indubitable, the architectural design and interpretability of such models are
nontrivial. Research has been introduced to automate the design of neural
network architectures through neural architecture search (NAS). Recent progress
has made these methods more pragmatic by exploiting distributed computation and
novel optimization algorithms. However, there is little work in optimizing
architectures for interpretability. To this end, we propose a multi-objective
distributed NAS framework that optimizes for both task performance and
introspection. We leverage the non-dominated sorting genetic algorithm
(NSGA-II) and explainable AI (XAI) techniques to reward architectures that can
be better comprehended by humans. The framework is evaluated on several image
classification datasets. We demonstrate that jointly optimizing for
introspection ability and task error leads to more disentangled architectures
that perform within tolerable error.Comment: 14 pages main text, 5 pages references, 17 pages supplementa