Quantifying Model Uniqueness in Heterogeneous AI Ecosystems

Model uniqueness cannot be determined from observation alone: The paper proves that analyzing how AI models behave on existing data is mathematically insufficient to determine whether they're truly unique or just redundant copies. Regulators and organizations need to actively test models with new scenarios to make this determination.

Active testing can efficiently identify redundant models: The research demonstrates that a strategic testing approach can identify which models are genuinely novel versus functionally replaceable by combinations of existing models, using a sample-efficient protocol that scales predictably with the number of models being audited.

Popular fairness metrics miss redundancy: The paper shows that Shapley values—a widely-used method for explaining AI model contributions and fairness—fundamentally fail to detect when models are redundant with each other, suggesting current governance tools may not catch unnecessary duplication in AI systems.

Redundancy matters for responsible AI governance: As organizations deploy multiple AI models (foundation models plus specialized versions), distinguishing genuine innovation from functional redundancy becomes critical for managing risks, ensuring accountability, and avoiding unnecessary complexity in AI ecosystems.

Framework tested across real AI systems: The researchers validated their approach on actual computer vision models (ResNet, ConvNeXt, ViT), language models (BERT, RoBERTa), and traffic prediction systems, demonstrating practical applicability beyond theoretical scenarios.