  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.
