Ex Ante Evaluation of AI-Induced Idea Diversity Collapse
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 compariso...
| Authors | Nafis Saami Azad, Raiyan Abdul Baten |
|---|---|
| Primary category | cs.AI |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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