Human decision-makers often face choices about complex cases with many potentially relevant features, but limited bandwidth to inspect and integrate all available information. In such settings, we study algorithms that highlight a small subset of case-specific features for human consideration, rather than producing a single prediction or recommendation. We model highlighting as a constrained information policy that selects a small number of features to reveal. A central issue is how humans interpret the algorithm's choice of features: a sophisticated agent correctly conditions on the selection rule, while a naive agent updates only on revealed feature values and treats the selection event as exogenous. We show that optimizing highlighting for sophisticated agents can be computationally intractable, even in simple discrete and binary settings, whereas optimizing for naive agents is tractable as long as the maximal bandwidth is fixed. We also show that a highlighting policy that is optimal for sophisticated agents can perform arbitrarily poorly when deployed to naive agents, motivating robust, implementable alternatives. We illustrate our framework in a calibrated empirical exercise based on the American Housing Survey. Overall, our results establish the value of highlighting a context-specific set of features rather than a fixed one as a practically appealing and computationally feasible tool for achieving human-algorithm complementarity.

翻译：人类决策者常需处理具有众多潜在相关特征的复杂案例，但其信息处理能力有限，难以全面审视并整合所有可用信息。在此背景下，我们研究如何通过算法高亮一小部分案例特定特征供人类参考，而非生成单一预测或建议。我们将特征高亮建模为一种受限信息策略，即选择少量特征予以揭示。核心问题在于人类如何解读算法的特征选择：复杂型智能体能够正确依据选择的规则进行条件推理，而朴素型智能体仅根据已揭示的特征值进行更新，将选择事件视为外生因素。研究表明，即使在简单的离散二值场景中，为复杂型智能体优化特征高亮策略在计算上可能难以处理；而只要最大信息带宽固定，为朴素型智能体优化特征高亮策略则是可处理的。此外，针对复杂型智能体最优的高亮策略，若部署于朴素型智能体，其表现可能极其糟糕，这促使我们需要稳健且可实施的替代方案。我们基于美国住房调查的校准实证研究展示了该框架的应用。总体而言，我们的结果证实：相比于固定特征集，高亮情境特定特征集作为一种兼具实践吸引力与计算可行性的工具，对于实现人机互补具有重要价值。