  Explainable Artificial Intelligence (XAI) plays a crucial role in enabling
human understanding and trust in deep learning systems. As models get larger,
more ubiquitous, and pervasive in aspects of daily life, explainability is
necessary to minimize adverse effects of model mistakes. Unfortunately, current
approaches in human-centric XAI (e.g. predictive tasks in healthcare,
education, or personalized ads) tend to rely on a single post-hoc explainer,
whereas recent work has identified systematic disagreement between post-hoc
explainers when applied to the same instances of underlying black-box models.
In this paper, we therefore present a call for action to address the
limitations of current state-of-the-art explainers. We propose a shift from
post-hoc explainability to designing interpretable neural network
architectures. We identify five needs of human-centric XAI (real-time,
accurate, actionable, human-interpretable, and consistent) and propose two
schemes for interpretable-by-design neural network workflows (adaptive routing
with InterpretCC and temporal diagnostics with I2MD). We postulate that the
future of human-centric XAI is neither in explaining black-boxes nor in
reverting to traditional, interpretable models, but in neural networks that are
intrinsically interpretable.
