提出可衡量解释质量的通用指标，让模型解释更符合人类理解。
A Human-Centered Validation of the Explainability-Performance Coefficient
- 用新指标EPC平衡特征稀疏性与模型性能，评估解释质量。
- 跨表格、文本、图像三类数据验证，高EPC值对应人类判断更一致。
- 适合关注模型可解释性可信度的研究者和应用开发者。
深度学习在高风险领域的广泛应用加剧了对可信可解释人工智能（XAI）的需求。然而，客观评估解释保真度并使XAI指标与人类认知对齐仍是重大挑战。本文提出一种模型无关的度量方法——扩展版解释-性能系数（EPC分数），通过显式权衡特征选择稀疏性与保留的模型性能，量化解释质量。在表格、文本和图像三类模态上的实证验证表明，EPC分数能有效揭示网络激活、数据维度与解释器表现之间的操作依赖关系。进一步地，通过独立的人类标注验证，高EPC分数与人类词汇情感判断及空间视觉标注高度一致，证明其具备良好的人类中心一致性。
原文摘要 · Abstract (English)
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.
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