Explainable Artificial Intelligence is critical in unraveling decision-making processes in complex machine learning models. LIME (Local Interpretable Model-agnostic Explanations) is a well-known XAI framework for image analysis. It utilizes image segmentation to create features to identify relevant areas for classification. Consequently, poor segmentation can compromise the consistency of the explanation and undermine the importance of the segments, affecting the overall interpretability. Addressing these challenges, we introduce DSEG-LIME (Data-Driven Segmentation LIME), featuring: i) a data-driven segmentation for human-recognized feature generation, and ii) a hierarchical segmentation procedure through composition. We benchmark DSEG-LIME on pre-trained models with images from the ImageNet dataset - scenarios without domain-specific knowledge. The analysis includes a quantitative evaluation using established XAI metrics, complemented by a qualitative assessment through a user study. Our findings demonstrate that DSEG outperforms in most of the XAI metrics and enhances the alignment of explanations with human-recognized concepts, significantly improving interpretability. The code is available under: https://github. com/patrick-knab/DSEG-LIME

翻译：可解释人工智能对于揭示复杂机器学习模型中的决策过程至关重要。LIME（局部可解释模型无关解释）是图像分析中著名的XAI框架。它利用图像分割创建特征，以识别分类相关的关键区域。因此，较差的分割会损害解释的一致性并降低片段的重要性，影响整体可解释性。针对这些挑战，我们提出了DSEG-LIME（数据驱动分割LIME），其特点包括：i）用于生成人类可识别特征的数据驱动分割，以及ii）通过组合实现的分层分割流程。我们在基于ImageNet数据集图像的预训练模型上对DSEG-LIME进行了基准测试——场景中不包含领域特定知识。分析包括使用既定XAI指标的定量评估，以及通过用户研究进行的定性评估。我们的结果表明，DSEG在大多数XAI指标上表现更优，并增强了解释与人类可识别概念的对齐，显著提高了可解释性。代码可在以下地址获取：https://github.com/patrick-knab/DSEG-LIME