AI，请掌舵：人机协同问答中的委托与信任驱动因素探究

摘要

人工智能系统并非完美无缺，人类在判断是否应信任AI而非自身判断时也可能犯错。因此，提升人机协作水平需要理解人类在何时、因何以及如何决定依赖AI。我们研究两种截然不同的依赖决策：授权选择——决定何时让AI在未知其输出结果的情况下自主行动；以及采纳选择——评估AI建议并决定如何运用这些建议。这两种解耦的依赖模式共同塑造了协作过程，但此前研究极少在真实场景中对相同用户同时考察这两类决策。为弥补这一空白，我们通过研究人机协作团队在问答游戏中的竞争关系展开分析——人类参与者可自主选择何时及如何与AI代理合作以赢得比赛。在24场对决中，23位专家级人类用户与16个AI代理组队协作，我们共采集了387次授权决策与1440次采纳决策。研究显示，虽然人机协作的整体表现优于纯人类或纯AI系统，但人类在协作决策中仍存在次优选择：既存在对正确AI建议的低度依赖（错失3.9%的潜在机会），也存在因AI误导导致的过度依赖（占比1.7%）。双方都会贡献错误答案：当人类与AI意见相左时，模型报告的置信度接近随机水平；而确认偏误则导致当AI建议与人类初始错误答案一致时，低度依赖比例显著升高至64.5%。为弥合这一差距，我们建议采用校准的置信度、基于证据的解释机制，以及能帮助用户优化信任判断的工具。

English

AI systems are fallible, and humans can make mistakes in deciding whether to trust AI over their own judgment. Thus, improving human-AI collaboration requires understanding when, why, and how humans decide to rely on AI. We study two distinct reliance decisions: the delegation choice -- deciding when to let AI act autonomously without knowing its output, and the adoption choice -- evaluating AI suggestions and deciding how to use them. Both of these decoupled reliance patterns shape collaboration, but prior work rarely studies them together in realistic settings with the same users. We address this gap by studying collaborative human--AI teams competing in a question-answering game in which humans can choose when and how to work with AI agents to win. Our 24 matches pair 23 expert humans with 16 AI agents, capturing 387 delegation and 1440 adoption decisions. While human--AI collaboration performs better than either AI or humans alone, humans make suboptimal collaboration decisions, both under-relying on correct AI suggestions (3.9% of opportunities missed) and over-relying when AI misleads them (1.7%). Both parties contribute wrong answers: reported model confidence is near chance when humans and AI disagree, while confirmation bias drives higher under-reliance (64.5%) when an AI suggestion agrees with humans' initial incorrect answer. To close this gap, we recommend calibrated confidence, evidence-grounded explanations, and mechanisms that help users refine trust.