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) Common restrictions on algorithms, such as keeping a "human-in-the-loop" or requiring maximal prediction accuracy, strictly worsen decision quality in the absence of perfectly aligned agents and state-revealing signals. 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：委托人设计了一个算法，该算法生成一个关于二元状态的可公开观测的预测。她必须决定是直接基于该预测采取行动，还是将决策权委托给一个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委托规则的优化设计问题。三项核心发现如下：（1）当且仅当委托人若能观察到代理人信息时，会做出与代理人相同的二元决策时，委托为最优策略。（2）即便委托人能够基于算法预测采取行动，提供信息量最大的算法也可能并非最优。相反，最优算法可能针对某一状态提供更多信息，同时限制另一状态的信息输出。（3）常见的算法限制措施——例如保持"人类在环中"或要求最大预测精度——在缺乏完全目标一致的代理人与状态揭示信号的情况下，会严格损害决策质量。这些发现预测，若不采取措施缓解算法与人类决策者之间普遍存在的偏好偏差，人机协作将表现欠佳。