  In recent years, Artificial Intelligence (AI) algorithms have been proven to
outperform traditional statistical methods in terms of predictivity, especially
when a large amount of data was available. Nevertheless, the "black box" nature
of AI models is often a limit for a reliable application in high-stakes fields
like diagnostic techniques, autonomous guide, etc. Recent works have shown that
an adequate level of interpretability could enforce the more general concept of
model trustworthiness. The basic idea of this paper is to exploit the human
prior knowledge of the features' importance for a specific task, in order to
coherently aid the phase of the model's fitting. This sort of "weighted" AI is
obtained by extending the empirical loss with a regularization term encouraging
the importance of the features to follow predetermined constraints. This
procedure relies on local methods for the feature importance computation, e.g.
LRP, LIME, etc. that are the link between the model weights to be optimized and
the user-defined constraints on feature importance. In the fairness area,
promising experimental results have been obtained for the Adult dataset. Many
other possible applications of this model agnostic theoretical framework are
described.
