High-stakes decision-making is often compromised by cognitive biases and outcome dependency. Current AI models typically mimic historical human behavior, inheriting these biases and limiting their utility for normative improvement. Here, we introduce a Prescriptive AI framework designed to audit, rather than automate, human judgment in real-time environments. By decoupling decision quality from stochastic outcomes, we quantify "decision latency" and status quo bias in elite soccer management - a high-pressure adversarial domain. Analyzing 2018 FIFA World Cup data, our system exposes critical risk states, such as performance collapse following salient positive events (e.g., an assist), which human experts systematically overlook due to outcome bias. These findings demonstrate that interpretable auditing systems can reveal structural flaws in human reasoning that predictive models obscure. This approach establishes a paradigm for Human-AI interaction prioritizing epistemic accountability over predictive mimicry in safety-critical domains.

翻译：高风险决策常受认知偏差与结果依赖性的影响。现有AI模型通常模仿历史人类行为，继承了这些偏差并限制了其规范性改进的效用。本文提出一种规范性人工智能框架，旨在对实时环境中的人类判断进行审计而非自动化替代。通过将决策质量与随机结果解耦，我们量化了精英足球管理这一高压对抗领域中的"决策延迟"与现状偏差。基于2018年国际足联世界杯数据的分析表明，我们的系统能揭示关键风险状态，例如显著积极事件（如助攻）后的表现崩溃，而人类专家因结果偏差会系统性忽视这些状态。这些发现证明，可解释的审计系统能够揭示人类推理中的结构性缺陷，而预测模型往往会掩盖这些缺陷。该方法为安全关键领域的人机交互建立了新范式，将认知问责置于预测模仿之上。