arXiv:2605.12340v2 Announce Type: replace-cross Abstract: Learning-to-Defer (L2D) methods route each query either to a predictive model or to external experts. While existing work studies this problem in batch settings, real-world deployments require handling streaming data, changing expert availability, and shifting expert distribution. We introduce the first online L2D algorithm for multiclass classification with bandit feedback and a dynamically varying pool of experts. Our method achieves regret guarantees of $O((n+n_e)T^{2/3})$ in general and $O((n+n_e)\sqrt{T})$ under a low-noise conditi
The proliferation of complex AI models and the increasing need for reliable decision-making in dynamic environments necessitates advanced methods for human-AI collaboration.
This research addresses a critical gap in real-world AI deployment by enabling models to collaborate effectively with human experts, enhancing robustness and adaptability in crucial applications.
AI systems can now dynamically decide whether to act autonomously or defer to human experts, even when those experts change or their performance shifts, leading to more resilient and ethical deployments.
- · AI-powered service industries
- · Healthcare providers
- · Financial services
- · Ethical AI developers
- · Monolithic AI-only solutions
- · Systems with static expert fallback
- · High-risk manual decision processes
Improved reliability and trust in AI systems due to dynamic expert oversight.
Reduced operational costs and increased efficiency as AI and human expertise are optimally allocated.
The acceleration of AI deployment into highly regulated and sensitive domains where human-in-the-loop validation is paramount.
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Read at arXiv cs.LG