Realizable $H$-Consistent and Bayes-Consistent Loss Functions for Learning to Defer
This work addresses the theoretical foundation for learning to defer, which is important for AI systems that can defer decisions to humans, but it is incremental as it builds on existing frameworks.
The paper tackles the problem of designing surrogate loss functions for learning to defer, establishing realizable H-consistency and Bayes-consistency under mild conditions, and resolving an open question from prior work.
We present a comprehensive study of surrogate loss functions for learning to defer. We introduce a broad family of surrogate losses, parameterized by a non-increasing function $Ψ$, and establish their realizable $H$-consistency under mild conditions. For cost functions based on classification error, we further show that these losses admit $H$-consistency bounds when the hypothesis set is symmetric and complete, a property satisfied by common neural network and linear function hypothesis sets. Our results also resolve an open question raised in previous work (Mozannar et al., 2023) by proving the realizable $H$-consistency and Bayes-consistency of a specific surrogate loss. Furthermore, we identify choices of $Ψ$ that lead to $H$-consistent surrogate losses for any general cost function, thus achieving Bayes-consistency, realizable $H$-consistency, and $H$-consistency bounds simultaneously. We also investigate the relationship between $H$-consistency bounds and realizable $H$-consistency in learning to defer, highlighting key differences from standard classification. Finally, we empirically evaluate our proposed surrogate losses and compare them with existing baselines.