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This paper introduces idSCD, a white-box method that uses semantic correlation descriptors to identify whether a dataset was used in training a model, outperforming existing baselines across multiple settings.
This paper introduces I-SAFE, a post-hoc distributional auditing framework for scientific AI models using Wasserstein Coherence Metrics, which reveals structural differences in model outputs that accuracy-based evaluation fails to capture. Demonstrated on drug-target interaction prediction, the framework is model-agnostic and applicable to any domain with structured inputs and external priors.