Recent work has proposed artificial intelligence (AI) models that can learn to decide whether to make a prediction for an instance of a task or to delegate it to a human by considering both parties' capabilities. In simulations with synthetically generated or context-independent human predictions, delegation can help improve the performance of human-AI teams -- compared to humans or the AI model completing the task alone. However, so far, it remains unclear how humans perform and how they perceive the task when they are aware that an AI model delegated task instances to them. In an experimental study with 196 participants, we show that task performance and task satisfaction improve through AI delegation, regardless of whether humans are aware of the delegation. Additionally, we identify humans' increased levels of self-efficacy as the underlying mechanism for these improvements in performance and satisfaction. Our findings provide initial evidence that allowing AI models to take over more management responsibilities can be an effective form of human-AI collaboration in workplaces.

翻译：近期研究提出了一种人工智能（AI）模型，该模型能够通过评估双方能力，自主决策是否对特定任务实例进行预测或将其委派给人类。在基于合成数据或情境无关的人类预测模拟实验中，与仅由人类或AI单独完成任务相比，委派机制可提升人机协作团队的整体表现。然而，目前尚不明确的是：当人类意识到AI模型将任务实例委派给他们时，其实际表现与主观感知会如何变化。基于一项包含196名参与者的实验研究，我们发现：无论参与者是否知晓委派机制的存在，AI委派都能显著提升任务绩效与任务满意度。进一步研究表明，人类自我效能感的提升是驱动上述绩效与满意度改善的核心机制。本研究成果为以下观点提供了初步证据：允许AI模型承担更多管理职责，是工作场所中人机协作的一种有效形式。