Explainable Artificial Intelligence (XAI) plays a crucial role in enabling
human understanding and trust in deep learning systems, often defined as
determining which features are most important to a model's prediction. As
models get larger, more ubiquitous, and pervasive in aspects of daily life,
explainability is necessary to avoid or 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 explainer. This is a particularly concerning trend when considering
that recent work has identified systematic disagreement in explainability
methods when applied to the same points and 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 to shift from post-hoc
explainability to designing interpretable neural network architectures; moving
away from approximation techniques in human-centric and high impact
applications. 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 for
interpretable conditional computation and diagnostic benchmarks for iterative
model learning). 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.

该论文呼吁从事人类中心的可解释人工智能研究，探究人类对于深度学习系统的理解和信任的关键作用，并提出设计可解释的神经网络体系结构的方案，以实现实时、准确、可操作、易于解释和一致的需求。