As humans increasingly rely on multiround conversational AI for high stakes decisions, principled frameworks are needed to ensure such interactions reliably improve decision quality. We adopt a human centric view governed by two principles: counterfactual harm, ensuring the AI does not undermine human strengths, and complementarity, ensuring it adds value where the human is prone to err. We formalize these concepts via user defined rules, allowing users to specify exactly what harm and complementarity mean for their specific task. We then introduce an online, distribution free algorithm with finite sample guarantees that enforces the user-specified constraints over the collaboration dynamics. We evaluate our framework across two interactive settings: LLM simulated collaboration on a medical diagnostic task and a human crowdsourcing study on a pictorial reasoning task. We show that our online procedure maintains prescribed counterfactual harm and complementarity violation rates even under nonstationary interaction dynamics. Moreover, tightening or loosening these constraints produces predictable shifts in downstream human accuracy, confirming that the two principles serve as practical levers for steering multi-round collaboration toward better decision quality without the need to model or constrain human behavior.

翻译：随着人类在高风险决策中日益依赖多轮对话式人工智能，需要建立原则性框架以确保此类交互可靠提升决策质量。我们采用以人为中心的视角，遵循两项核心原则：反事实损害原则——确保人工智能不会削弱人类优势；互补性原则——确保其在人类易出错领域提供价值。我们通过用户定义规则形式化这些概念，允许用户针对具体任务明确定义损害与互补性的具体含义。随后提出一种具有有限样本保证的在线无分布算法，该算法能在协作动态过程中强制执行用户指定的约束条件。我们在两种交互场景中评估该框架：医疗诊断任务的LLM模拟协作，以及图像推理任务的人类众包研究。实验表明，即使在非平稳交互动态下，我们的在线程序仍能维持预设的反事实损害与互补性违规率。此外，收紧或放松这些约束会产生可预测的下游人类准确率变化，证实这两项原则可作为实用调控手段，引导多轮协作走向更优决策质量，而无需对人类行为进行建模或约束。