Abstract Deep Learning methods are well-known for their abilities, but interpretability keeps them out of high-stakes situations. This difficulty is addressed by recent model-agnostic that provide explanations after the training process. As a result, current guidelines’ requirement “interpretability from start” not met. such only useful as sanity check model has been trained. In an abstract sce...

The word Interpretability is becoming more and more frequent in the fuzzy literature. It is admitted as the main advantage of fuzzy systems and it should be given a main role in fuzzy modeling. However, although researchers talk a lot about Interpretability, it is even not clear what it really means. Understanding of fuzzy systems is a subjective task which strongly depends on the person (exper...

Supervised machine learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can it tell you about the world? We want models to be not only good, but interpretable. And yet the task of interpretation appears underspecified. Papers provide diverse and sometimes non-overlapping motivations for interpretability, and offer myriad n...

This paper presents amethod for an automatic and complete design of fuzzy systems fromdata. Themain objective is to build fuzzy systems with a user-controllable trade-off between accuracy and interpretability. Whereas criteria for accuracy mostly follow straightforwardly from the application, definition of interpretability and its criteria are subject to controversial discussion. For this reaso...

This contribution is concerned with the interpretability of fuzzy rule-based systems. While this property is widely considered to be a crucial one in fuzzy rule-based modeling, a more detailed formal investigation of what "interpretability" actually means is not available. So far, interpretability has most often been associated with rather heuristic assumptions about shape and mutual overlappin...

This paper discusses interpretability in two main categories of fuzzy systems fuzzy rule-based classifiers and interpolative fuzzy systems. Our goal is to show that the aspect of high level interpretability is more relevant to fuzzy classifiers, whereas fuzzy systems employed in modeling and control benefit more from low-level interpretability. We also discuss the interpretabilityaccuracy trade...

We adopt a theory of relativisation based on the idea that relatives, like wh-constructions in the analysis of Chomsky (1998), require two sorts of features to construct their LF-interpretation. We argue that it is the variable interpetability of these features that gives rise to different syntactic patterns. We use this theory to provide an explanation for some curious syntactic facts found in...

This paper reviews recent studies in emerging directions of understanding neural-network representations and learning neural networks with interpretable/disentangled middle-layer representations. Although deep neural networks have exhibited superior performance in various tasks, the interpretability is always an Achilles' heel of deep neural networks. At present, deep neural networks obtain a h...