A Human-Centered Validation of the Explainability-Performance Coefficient

As deep learning enters high-stakes domains like healthcare and finance, trustworthy explanations become essential --- yet measuring whether an explanation is actually good remains unresolved. This paper validates an EPC score that balances how sparse a feature explanation is against how much model performance it preserves. Tested across tabular, text, and image data, higher EPC scores align well with human judgments, including sentiment interpretation and visual annotation. This offers practitioners a model-agnostic, human-validated way to evaluate explainability tools before deploying AI systems where interpretability affects trust and accountability. Authors: Christian Oliva, Luis F. Lago-Fernández Paper: https://arxiv.org/abs/2607.29614v1