We argue that synthesizing insights from humans and ML models at the level of features is an important direction to explore to improve human-ML collaboration on decision-making problems. We show through an illustrative example that feature-level synthesis can produce correct predictions in a case where existing methods fail, then lay out directions for future exploration.
