I-SAFE uses Wasserstein Coherence Metrics to audit distributional coherence of scientific AI models under structurally guided perturbations, revealing differences among DTI predictors that accuracy metrics miss.
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Towards A Rigorous Science of Interpretable Machine Learning
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abstract
As machine learning systems become ubiquitous, there has been a surge of interest in interpretable machine learning: systems that provide explanation for their outputs. These explanations are often used to qualitatively assess other criteria such as safety or non-discrimination. However, despite the interest in interpretability, there is very little consensus on what interpretable machine learning is and how it should be measured. In this position paper, we first define interpretability and describe when interpretability is needed (and when it is not). Next, we suggest a taxonomy for rigorous evaluation and expose open questions towards a more rigorous science of interpretable machine learning.
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representative citing papers
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A latent mediation framework with sparse autoencoders enables non-additive token-level influence attribution in LLMs by learning orthogonal features and back-propagating attributions.
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ShifaMind achieves competitive performance with the LAAT baseline on MIMIC-IV top-50 ICD-10 coding while outperforming vanilla concept bottleneck models and providing concept-mediated explanations.
The authors introduce the XAI Evaluation Card template to standardize how XAI evaluation metrics are defined, validated, and reported.
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