AI systems increasingly support human decision-making. In many cases, despite the algorithm's superior performance, the final decision remains in human hands. For example, an AI may assist doctors in determining which diagnostic tests to run, but the doctor ultimately makes the diagnosis. This paper studies such AI-assisted decision-making settings, where the human learns through repeated interactions with the algorithm. In our framework, the algorithm -- designed to maximize decision accuracy according to its own model -- determines which features the human can consider. The human then makes a prediction based on their own less accurate model. We observe that the discrepancy between the algorithm's model and the human's model creates a fundamental tradeoff: Should the algorithm prioritize recommending more informative features, encouraging the human to learn their importance, even if it results in less accurate predictions in the short term until learning occurs? Or is it preferable to forgo educating the human and instead select features that align more closely with their existing understanding, minimizing the immediate cost of learning? Our analysis reveals how this trade-off is shaped by both the algorithm's patience (the time-discount rate of its objective over multiple periods) and the human's willingness and ability to learn. We show that optimal feature selection has a surprisingly clean combinatorial characterization, reducible to a stationary sequence of feature subsets that is tractable to compute. As the algorithm becomes more "patient" or the human's learning improves, the algorithm increasingly selects more informative features, enhancing both prediction accuracy and the human's understanding.

翻译：人工智能系统日益支持人类决策。在许多情况下，尽管算法性能优越，最终决策权仍掌握在人类手中。例如，AI可以辅助医生确定应进行哪些诊断检测，但最终诊断仍由医生作出。本文研究此类人工智能辅助决策场景，其中人类通过与算法的重复交互进行学习。在我们的框架中，算法——根据其自身模型以最大化决策准确性为目标——决定人类可考虑的特征。随后，人类基于其自身准确性较低的模型进行预测。我们观察到，算法模型与人类模型之间的差异产生了根本性的权衡：算法是否应优先推荐信息量更大的特征，以鼓励人类学习其重要性，即使这在学习发生前的短期内会导致预测准确性下降？抑或放弃教育人类，转而选择更符合其现有理解的特征，以最小化即时学习成本更为可取？我们的分析揭示了这种权衡如何同时受算法耐心度（其目标在多周期内的时间折现率）以及人类学习意愿与能力的影响。研究表明，最优特征选择具有出人意料简洁的组合特征表征，可简化为易于计算的静态特征子集序列。随着算法变得更具"耐心"或人类学习能力提升，算法会越来越多地选择信息量更大的特征，从而同时提升预测准确性和人类认知水平。