Individual human decision-makers may benefit from different forms of support to improve decision outcomes, but when each form of support will yield better outcomes? In this work, we posit that personalizing access to decision support tools can be an effective mechanism for instantiating the appropriate use of AI assistance. Specifically, we propose the general problem of learning a decision support policy that, for a given input, chooses which form of support to provide to decision-makers for whom we initially have no prior information. We develop $\texttt{Modiste}$, an interactive tool to learn personalized decision support policies. $\texttt{Modiste}$ leverages stochastic contextual bandit techniques to personalize a decision support policy for each decision-maker and supports extensions to the multi-objective setting to account for auxiliary objectives like the cost of support. We find that personalized policies outperform offline policies, and, in the cost-aware setting, reduce the incurred cost with minimal degradation to performance. Our experiments include various realistic forms of support (e.g., expert consensus and predictions from a large language model) on vision and language tasks. Our human subject experiments validate our computational experiments, demonstrating that personalization can yield benefits in practice for real users, who interact with $\texttt{Modiste}$.

翻译：个体决策者可能受益于不同形式的支持以改善决策结果，但何时每种支持形式将产生更好的结果？在本研究中，我们提出，个性化提供决策支持工具可以成为实现人工智能辅助适当使用的有效机制。具体而言，我们提出了学习决策支持策略的一般性问题：对于给定输入，该策略为初始无先验信息的决策者选择提供何种形式的支持。我们开发了 $\texttt{Modiste}$，一种用于学习个性化决策支持策略的交互式工具。$\texttt{Modiste}$ 利用随机上下文赌博机技术为每位决策者个性化定制决策支持策略，并支持扩展到多目标场景以兼顾辅助目标（如支持成本）。我们发现个性化策略优于离线策略，且在成本敏感场景中能以最小性能损失降低支持成本。我们的实验涵盖了视觉和语言任务上多种现实支持形式（例如专家共识和大型语言模型预测）。我们的人类受试者实验验证了计算实验的结果，表明个性化能够为实际使用 $\texttt{Modiste}$ 的真实用户带来实践效益。