eXplainable Artificial Intelligence (XAI) aims at providing understandable explanations of black box models. In this paper, we evaluate current XAI methods by scoring them based on ground truth simulations and sensitivity analysis. To this end, we used an Electric Arc Furnace (EAF) model to better understand the limits and robustness characteristics of XAI methods such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), as well as Averaged Local Effects (ALE) or Smooth Gradients (SG) in a highly topical setting. These XAI methods were applied to various types of black-box models and then scored based on their correctness compared to the ground-truth sensitivity of the data-generating processes using a novel scoring evaluation methodology over a range of simulated additive noise. The resulting evaluation shows that the capability of the Machine Learning (ML) models to capture the process accurately is, indeed, coupled with the correctness of the explainability of the underlying data-generating process. We furthermore show the differences between XAI methods in their ability to correctly predict the true sensitivity of the modeled industrial process.

翻译：可解释人工智能（XAI）旨在为黑盒模型提供可理解的解释。本文通过基于真实仿真和敏感性分析的评分体系对当前XAI方法进行评估。为此，我们采用电弧炉（EAF）模型，在高度聚焦的工业场景中深入探究SHAP、LIME以及ALE和SG等XAI方法的局限性及鲁棒性特征。这些XAI方法被应用于多种类型的黑盒模型，随后通过创新的评分评估方法——在模拟加性噪声范围内对比数据生成过程的真实敏感性——对其解释正确性进行量化评分。评估结果表明：机器学习模型准确捕捉过程的能力确实与底层数据生成过程的可解释正确性存在关联。我们进一步揭示了不同XAI方法在准确预测建模工业过程真实敏感性方面的能力差异。