As AI solutions become ever more ubiquitous, there is still a serious lack of understanding in how these systems make decisions. The brittle nature of the statistical correlations learned by these models are often overlooked. To address this gap, Explainable and Interpretable Artificial Intelligence (XAI) research seeks to explain how and why models make their decisions.  Without this understanding, users of AI systems are left blindly trusting the output of their AI models.  Especially in high-stakes decision-making such as with self-driving cars and criminal sentencing, XAI methods provide trust anchors in how these systems work in order to verify the validity of these decisions and debug them when they are not.

One of FINS faculty’s thrust on this topic focuses on interpretable methods for natural language processing.  We distinguish traditional XAI and inherently interpretable AI architectures.  XAI has primarily focused on post-hoc, opaque-box approaches, which can be misleading as they themselves are approximations of the model they are attempting to explain.  This work focuses on the design of powerful inherently interpretable methods that combine the clarity of decision trees with the power of deep learning neural architectures.