Unpacking Interpretability: Human-Centered Criteria for Optimal Combinatorial Solutions

ArXi:2603.08856v1 Announce Type: cross Algorithmic systems often return optimal solutions that are hard to understand. Effective human-algorithm collaboration, however, requires interpretability. When machine solutions are equally optimal, humans must select one, but a precise account of what makes one solution interpretable than another remains missing. To identify structural properties of interpretable machine solutions, we present an experimental paradigm in which participants chose which of two equally optimal solutions for packing items into bins was easier to understand.