Despite advances in AI's performance and interpretability, AI advisors can undermine experts' decisions and increase the time and effort experts must invest to make decisions. Consequently, AI systems deployed in high-stakes settings often fail to consistently add value across contexts and can even diminish the value that experts alone provide. Beyond harm in specific domains, such outcomes impede progress in research and practice, underscoring the need to understand when and why different AI advisors add or diminish value. To bridge this gap, we stress the importance of assessing the value AI advice brings to real-world contexts when designing and evaluating AI advisors. Building on this perspective, we characterize key pillars -- pathways through which AI advice impacts value -- and develop a framework that incorporates these pillars to create reliable, personalized, and value-adding advisors. Our results highlight the need for system-level, value-driven development of AI advisors that advise selectively, are tailored to experts' unique behaviors, and are optimized for context-specific trade-offs between decision improvements and advising costs. They also reveal how the lack of inclusion of these pillars in the design of AI advising systems may be contributing to the failures observed in practical applications.

翻译：尽管人工智能的性能与可解释性不断进步，AI顾问仍可能损害专家的决策质量，并增加专家制定决策所需投入的时间与精力。因此，部署在高风险场景中的AI系统往往无法在不同情境中持续创造价值，甚至可能削弱专家独立提供的价值。除了在特定领域造成损害外，此类结果还会阻碍研究与实践的进展，这凸显了理解不同AI顾问何时以及为何增加或削弱价值的必要性。为弥合这一差距，我们强调在设计与评估AI顾问时，必须评估AI建议在现实情境中所带来的价值。基于这一视角，我们界定了关键支柱——即AI建议影响价值的路径——并构建了一个整合这些支柱的框架，以创建可靠、个性化且能持续增值的顾问系统。我们的研究结果凸显了系统级、价值驱动型AI顾问开发的必要性：这类顾问应具备选择性建议能力，能适应专家独特的行为模式，并针对具体情境在决策改进与建议成本之间实现优化权衡。研究还揭示了，若在设计AI顾问系统时未纳入这些支柱，则可能导致实际应用中观察到的系统失效现象。