until all the possible explainable basis vectors have been identified. Although the human curation can doubtless improve the model interpretability, the author presents no study regarding the approach feasibility. Relying on such amount of human interaction could be a prohibitive costly task, given that vector representations are usually built over very large corpora.

DSMs are widely used as input features for machine learning models addressing a large variety of tasks. Using more coherent and interpretable data models can potentially increase the interpretability of the algorithmic models using them as input, providing an additional source of information for the generation of post-hoc explanations. We discuss interpretable algorithmic models in the next Section.

Algorithmic Models
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Interpretable algorithmic models are models which work by executing a sequence of computations over data, possibly using a set of parameters, to perform a given task. Different from data models, what matters here is not what they *are composed of*, but rather *how they work*.

**Fuzzy logic** is an example of a field of study where interpretability has long been a central concept. As observed by Alonso et al. [-@alonso2011special], “thanks to their semantic expressivity, close to natural language, fuzzy variables and rules can be used 