这篇论文给可解释性打分提出了一个新指标EPC，不挑模型，而且拿人类判断验证过，做XAI研究的可以看。
arXiv 论文提出EPC评分，这是对Explainability-Performance Coefficient的扩展，用模型无关方式量化解释质量，显式平衡特征选择稀疏性与模型性能保持。研究在表格、文本、图像三种模态上做了实证验证，发现EPC能揭示网络激活、数据维度和解释器性能之间的操作依赖。作者将EPC与独立人类解释对比，证明更高EPC评分与人类词汇情感判断及空间视觉注释有强一致性。
A Human-Centered Validation of the Explainability-Performance Coefficient
The rapid adoption of deep learning models in high-risk domains has intensified the need for trustworthy Explainable Artificial Intelligence (XAI). However, objectively evaluating explanation fidelity and aligning XAI metrics with human-centered understanding remain critical open challenges. In this work, we propose a model-agnostic metric, the EPC score, which is an extension of the Explainability-Performance Coefficient (EPC), that quantifies explanation quality by explicitly balancing the trade-off between feature selection sparsity and preserved model performance. Through an empirical validation across tabular, text, and image modalities, we show that the EPC score effectively uncovers operational dependencies among network activations, data dimensionality, and explainer performance. Furthermore, we validate the EPC score against independent human-based explanations, proving that higher EPC scores strongly align with human lexical sentiment judgments and spatial visual annotations.