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We propose Preferential MoE, a novel human-ML mixture-of-experts model thataugments human expertise in decision making with a data-based classifier onlywhen necessary for predictive performance. Our model exhibits an interpretablegating function that provides information on when human rules should befollowed or avoided. The gating function is maximized for using human-basedrules, and classification errors are minimized. We propose solving a coupledmulti-objective problem with convex subproblems. We develop approximatealgorithms and study their performance and convergence. Finally, we demonstratethe utility of Preferential MoE on two clinical applications for the treatmentof Human Immunodeficiency Virus (HIV) and management of Major DepressiveDisorder (MDD).

Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible