I-SAFE: Wasserstein Coherence Metrics for Structural Auditing of Scientific AI Models
Deep learning models are increasingly used in scientific prediction tasks where strong benchmark performance is often interpreted as evidence of scientifically meaningful behavior. This interpretation is fragile, as models may exploit shortcut features, dataset-specific regularities, or distributional biases that are predictive on held-out data but not aligned with domain-relevant structure. To address this limitation, we introduce the \textsc{I-SAFE} (Interventional Secure, Accurate, Fair and Explainable) framework, a post-hoc distributional auditing framework for scientific AI models centered on the Wasserstein Coherence Metric (WCM). Given a trained black-box predictor and an external ...
| Authors | Barbara Tarantino, Gennaro Auricchio, Paolo Giudici |
|---|---|
| Primary category | cs.LG |
| Published | 20 May 2026 |
| Last updated at source | 5 June 2026 |
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