  Interpretability can be critical for the safe and responsible use of machine
learning models in high-stakes applications. So far, evolutionary computation
(EC), in particular in the form of genetic programming (GP), represents a key
enabler for the discovery of interpretable machine learning (IML) models. In
this short paper, we argue that research in GP for IML needs to focus on
searching in the space of low-complexity models, by investigating new kinds of
search strategies and recombination methods. Moreover, based on our experience
of bringing research into clinical practice, we believe that research should
strive to design better ways of modeling and pursuing interpretability, for the
obtained solutions to ultimately be most useful.
