Collaboration with artificial intelligence (AI) has improved human decision-making across various domains by leveraging the complementary capabilities of humans and AI. Yet, humans systematically overrely on AI advice, even when their independent judgment would yield superior outcomes, fundamentally undermining the potential of human-AI complementarity. Building on prior work, we identify prevailing incentive structures in human-AI decision-making as a structural driver of this overreliance. To address this misalignment, we propose an alternative incentive mechanism designed to counteract systemic overreliance. We empirically evaluate this approach through a behavioral experiment with 180 participants, finding that the proposed mechanism significantly reduces overreliance. We also show that while appropriately designed incentives can enhance collaboration and decision quality, poorly designed incentives may distort behavior, introduce unintended consequences, and ultimately degrade performance. These findings underscore the importance of aligning incentives with task context and human-AI complementarities, and suggest that effective collaboration requires a shift toward context-sensitive incentive design.

翻译：与人工智能（AI）的协作通过发挥人类与AI的互补能力，已在多个领域提升了人类决策质量。然而，人类会系统性过度依赖AI建议——即使其独立判断本可产生更优结果——这从根本上破坏了人机互补的潜力。基于已有研究，我们识别出人机决策中的主流激励结构是造成这种过度依赖的结构性动因。为应对这一错位问题，我们提出了一种替代性激励机制，旨在系统性遏制过度依赖。通过一项包含180名参与者的行为实验，我们对该方法进行了实证评估，发现所提出的机制显著降低了过度依赖行为。同时研究表明，合理设计的激励机制能够增强协作并提升决策质量，但设计不当的激励可能扭曲行为、引发意外后果，并最终损害绩效。这些发现凸显了激励设计与任务情境及人机互补性对齐的重要性，并表明有效协作需要转向情境敏感的激励设计范式。