AI systems have the potential to improve decision-making, but decision makers face the risk that the AI may be misaligned with their objectives, which can lead them to restrict the information that the AI can use. We study the resulting "covariate-design'' problem in the context of a treatment decision, where a designer decides which patient attributes to reveal to an AI before receiving a prediction of the patient's need for treatment. Providing the AI with more information increases the benefits of an aligned AI but also amplifies the harm from a misaligned one. We characterize how the designer should select attributes to balance these competing forces, depending on their beliefs about the AI's reliability. We show that the designer should optimally disclose attributes that identify rare segments of the population in which the need for treatment is high, and pool the remaining patients.