example, in an evaluation challenge asking participants to rate the semantic similarity of pieces of texts and explain their decisions [@agirre2015semeval], approaches as different as knowledge base search [@hanig2015exb; @hassan2015fcicu; @banjade2015nerosim], rule-based [@banjade2015nerosim; @karumuri2015umduluth], referential translation machine [@biccici2015rtm], and support vector machine [@agirre2015ubc] were employed to implement an interpretable text analysis system. Interpretability is an AI concern rather than purely a machine learning matter. Therefore, in this work we seek to offer a broader view on interpretability, analyzing the efforts of different types of AI models to become more interpretable and how the concept of interpretability is dealt with by each of them.

We start by examining the concept of interpretability itself: how it is regarded across different fields and what shapes it can assume. We then analyze several models that claim to be interpretable and the evaluation methods and initiatives intended to measure a model’s level of interpretability. Finally, we look at the human-centric aspect of semantic interpretability, classifying models according to how they implement and what they offer as interpretations and pointing to gaps that still need to be addressed to meet explanation requirements from the final user point of view. The list of analyzed models is by 