It depends on the angle that people approach solving the problem. In my current field in cancer biology / drug response, people don't often know the features well enough comparing to normal everyday features such as natural images or natural text. In that setting the understanding of the feature space / biological systems is more important than understanding of the models themselves. The models are (if I may say, merely) a tool to search and narrow down the factors. After that scientists can design experiments to further interrogate the complex system itself. As the ML model grows bigger, the interrogative space also grow. Depending on the goal, it may not be necessary to have a fully interpretable model as long as the features themselves help advancing the understanding of the complex biology system.