创造性的人工智能系统通常在个人效用水平上进行评估,，但创造性的产出是在人群中消耗的:，当许多其他人产生类似的想法时，一个想法就会失去价值。这造成了评估盲点,，因为人工智能可以提高个人产出，同时增加人口水平的拥挤。我们引入了一个与人类相关的框架，用于对人工智能引起的人类多样性崩溃进行基准测试，而不需要人类与人工智能交互数据,，提供一个事前协议来估计仅模型世代的拥挤风险，并匹配无帮助的人类基线。通过将想法建模为可拥挤资源,，我们表明可以从分布内比较, 中识别源级拥挤，从而产生过度拥挤系数 $\Delta$ 和人类相对多样性比率 $\rho$。我们证明 $\rho\ge1$ 是无过度拥挤奇偶条件，并将 $\Delta$ 连接到具有依赖于暴露的冗余成本的采用游戏。在短篇故事,营销口号,和替代用途任务,中，三个前沿法学硕士在拥挤的核心中低于同等水平。估计值随着可行的纯模型样本量而稳定。重要的是, 生成协议变体表明，可以通过有针对性的设计, 减少拥挤，从而使多样性崩溃成为具有群体意识的创意人工智能的可操作, 开发时评估目标。
Creative AI systems are typically evaluated at the level of individual utility, yet creative outputs are consumed in populations: an idea loses value when many others produce similar ones. This creates an evaluation blind spot, as AI can improve individual outputs while increasing population-level crowding. We introduce a human-relative framework for benchmarking AI-induced human diversity collapse without requiring human-AI interaction data, providing an ex ante protocol to estimate crowding risk from model-only generations and matched unaided human baselines. By modeling ideas as congestible resources, we show that source-level crowding is identifiable from within-distribution comparisons, yielding an excess-crowding coefficient $\Delta$ and a human-relative diversity ratio $\rho$. We show that $\rho\ge1$ is the no-excess-crowding parity condition and connect $\Delta$ to an adoption game with exposure-dependent redundancy costs. Across short stories, marketing slogans, and alternative-uses tasks, three frontier LLMs fall below parity across crowding kernels. Estimates stabilize with feasible model-only sample sizes. Importantly, generation-protocol variants show that crowding can be reduced through targeted design, making diversity collapse an actionable, development-time evaluation target for population-aware creative AI.
科目: 人工智能 (cs.AI); 计算机科学与博弈论 (cs.GT)
Subjects: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT)