Unpacking Interpretability: Human-Centered Criteria for Optimal Combinatorial Solutions
Algorithmic support 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 more 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. We show that preferences reliably track three quantifiable properties of solution structure: alignment with a greedy heuristic, simple with...
| Authors | Dominik Pegler, Frank Jäkel, David Steyrl et al. |
|---|---|
| Primary category | cs.HC |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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