arXiv:2606.06081v1 Announce Type: new Abstract: Appropriate reliance on AI advice has become a central research theme in human-AI collaboration. Existing frameworks have focused exclusively on point predictions as AI advice. However, set-valued AI advice (e.g., discrete sets or continuous intervals) is increasingly being used to communicate uncertainty and improve human decision making. In this paper, we develop the first formal framework for measuring appropriate reliance on set-valued AI advice within the sequential judge-advisor paradigm, spanning both classification and regression tasks. F
The proliferation of AI systems, especially in decision-making contexts, necessitates robust frameworks for understanding and managing human trust and reliance on AI advice, moving beyond simplistic point predictions.
This framework addresses a critical gap in AI safety and human-AI collaboration, enabling better design of AI systems that communicate uncertainty effectively and prevent both over-reliance and under-reliance, which is crucial for high-stakes applications.
The focus for evaluating AI advice shifts from singular point predictions to more nuanced set-valued advice, which better represents uncertainty and could lead to more trustworthy and effective human-AI decision-making workflows.
- · AI safety researchers
- · Developers of decision-support AI
- · Sectors with high-stakes AI use (e.g., healthcare, finance)
- · AI systems that only offer point predictions
- · Frameworks unable to handle set-valued advice
AI models will increasingly be designed to output set-valued advice, enhancing their utility in complex decision environments.
Improved human-AI collaboration in critical sectors, leading to more resilient and nuanced decision-making processes.
New regulatory and ethical guidelines will emerge around the communication of AI uncertainty and appropriate human responses, impacting AI deployment standards.
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Read at arXiv cs.AI