A principal designs an algorithm that generates a publicly observable prediction of a binary state. She must decide whether to act directly based on the prediction or to delegate the decision to an agent with private information but potential misalignment. We study the optimal design of the prediction algorithm and the delegation rule in such environments. Three key findings emerge: (1) Delegation is optimal if and only if the principal would make the same binary decision as the agent had she observed the agent's information. (2) Providing the most informative algorithm may be suboptimal even if the principal can act on the algorithm's prediction. Instead, the optimal algorithm may provide more information about one state and restrict information about the other. (3) Well-intentioned policies aiming to provide more information, such as keeping a "human-in-the-loop" or requiring maximal prediction accuracy, could strictly worsen decision quality compared to systems with no human or no algorithmic assistance. These findings predict the underperformance of human-machine collaborations if no measures are taken to mitigate common preference misalignment between algorithms and human decision-makers.

翻译：一位委托人设计了一个算法，该算法生成对二元状态的可公开观测的预测。她必须决定是根据该预测直接采取行动，还是将决策权委托给拥有私人信息但可能存在目标偏差的代理人。我们研究了此类环境中预测算法与授权规则的最优设计。三个关键发现如下：（1）当且仅当委托人在观察到代理人信息后会做出与代理人相同的二元决策时，授权才是最优的。（2）即使委托人能依据算法预测采取行动，提供信息量最大的算法也可能并非最优。相反，最优算法可能提供关于某一状态的更多信息，同时限制关于另一状态的信息。（3）旨在提供更多信息的善意政策（例如保持“人在回路中”或要求最大预测精度）相比于无人类或无算法辅助的系统，可能严格降低决策质量。这些发现预测，若未采取措施缓解算法与人类决策者之间常见的偏好偏差，人机协作将表现欠佳。