Algorithms are used to aid human decision makers by making predictions and recommending decisions. Currently, these algorithms are trained to optimize prediction accuracy. What if they were optimized to control final decisions? In this paper, we study a decision-aid algorithm that learns about the human decision maker and provides ''personalized recommendations'' to influence final decisions. We first consider fixed human decision functions which map observable features and the algorithm's recommendations to final decisions. We characterize the conditions under which perfect control over final decisions is attainable. Under fairly general assumptions, the parameters of the human decision function can be identified from past interactions between the algorithm and the human decision maker, even when the algorithm was constrained to make truthful recommendations. We then consider a decision maker who is aware of the algorithm's manipulation and responds strategically. By posing the setting as a variation of the cheap talk game [Crawford and Sobel, 1982], we show that all equilibria are partition equilibria where only coarse information is shared: the algorithm recommends an interval containing the ideal decision. We discuss the potential applications of such algorithms and their social implications.

翻译：算法通过作出预测和推荐决策来辅助人类决策者。当前，这些算法被训练以优化预测准确性。如果它们被优化以控制最终决策会怎样？本文研究了一种学习人类决策者特征并提供"个性化推荐"以影响最终决策的决策辅助算法。我们首先考虑固定的人类决策函数，该函数将可观测特征与算法推荐映射至最终决策。我们刻画了可实现最终决策完美控制的条件。在相当普遍的假设下，即使算法曾受限于作出真实推荐，人类决策函数的参数仍可从算法与人类决策者过往交互中识别。随后我们考虑意识到算法操纵并作出策略性回应的决策者。通过将该场景设定为廉价谈话博弈[Crawford and Sobel, 1982]的变体，我们证明所有均衡均为仅共享粗略信息的分区均衡：算法推荐包含理想决策的区间。我们讨论了此类算法的潜在应用及其社会影响。