A Human-Centered Validation of the Explainability-Performance Coefficient

AI 中文解读

AI又有了新突破！这次科学家们给人工智能发明了一把“评分尺”，叫EPC分数，专门用来衡量AI的“解释能力”够不够好。以前用AI做医疗诊断或贷款审批时，它虽然能给出答案，但没人清楚它依据什么判断，这在大风险场景下实在让人不放心。这个新指标就像老师批改作业一样，既看AI精简锁定关键因素的能力，又看它在精简后还能否保持准确的判断力，并且不再局限于单一类型数据，文本、图像、表格都通用。更难得的是，评分结果与真人给出的判断高度吻合——AI觉得重要的信息，人也觉得重要，这说明它不再是“黑箱”了。未来你在医院看到AI辅助诊疗，或者用AI智能助手做分析时，它会明确告诉你哪些因素影响了判断，一旦出错也更容易被及时发现，这让AI用起来更可靠也更值得信任。

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.