arXiv:2603.14324v3 Announce Type: replace-cross Abstract: Learning-to-Defer routes each input to the expert that minimizes expected cost, but it assumes that the information available to every expert is fixed at decision time. Many modern systems violate this assumption: after selecting an expert, one may also choose what additional information that expert should receive, such as retrieved documents, tool outputs, or escalation context. We study this problem and call it Learning-to-Defer with advice. We show that a broad family of natural separated surrogates, which learn routing and advice wi
The proliferation of advanced AI systems and the increasing complexity of decision-making processes necessitate more sophisticated deferral mechanisms where human experts can interact dynamically with AI advice.
This research directly addresses a critical limitation in current AI deployment, moving beyond static deferral to systems where AI can actively inform human experts, leading to more robust and adaptable hybrid intelligence systems.
The paradigm for human-AI collaboration evolves from simple hand-off to dynamic, expert-conditional advice, enabling more nuanced and effective decision-making in complex environments.
- · AI developers
- · Enterprises deploying hybrid human-AI systems
- · Sectors requiring high-stakes decision-making
- · Systems relying on static AI deferral architectures
- · Human experts unwilling to integrate AI advice
Improved performance and reliability of AI-augmented decision-making systems across various industries.
Increased trust and adoption of AI in critical functions as systems become more adaptive and explainable through expert interaction.
The acceleration of 'AI agents' that can proactively provide context-rich advice and even 'learn' from expert interactions to improve their guidance.
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Read at arXiv cs.LG