A perturbation-based metric for XAI quality that formalizes sufficiency and necessity, paired with an adapter trained via differentiable supervision to generate causal explanations on black-box models.
Advances in neural information processing systems 31 (2018)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.AI 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
This survey synthesizes XAI methods with surrogate modeling workflows for simulations and outlines a research agenda to embed explainability into simulation-driven design and decision-making.
citing papers explorer
-
Learning Quantifiable Visual Explanations Without Ground-Truth
A perturbation-based metric for XAI quality that formalizes sufficiency and necessity, paired with an adapter trained via differentiable supervision to generate causal explanations on black-box models.
-
Interpretable and Explainable Surrogate Modeling for Simulations: A State-of-the-Art Survey and Perspectives on Explainable AI for Decision-Making
This survey synthesizes XAI methods with surrogate modeling workflows for simulations and outlines a research agenda to embed explainability into simulation-driven design and decision-making.