  Amid a discussion about Green AI in which we see explainability neglected, we
explore the possibility to efficiently approximate computationally expensive
explainers. To this end, we propose the task of feature attribution modelling
that we address with Empirical Explainers. Empirical Explainers learn from data
to predict the attribution maps of expensive explainers. We train and test
Empirical Explainers in the language domain and find that they model their
expensive counterparts well, at a fraction of the cost. They could thus
mitigate the computational burden of neural explanations significantly, in
applications that tolerate an approximation error.
