We present PrivLEX, a novel image privacy classifier that grounds its decisions in legally defined personal data concepts. PrivLEX is the first interpretable privacy classifier aligned with legal concepts that leverages the recognition capabilities of Vision-Language Models (VLMs). PrivLEX relies on zero-shot VLM concept detection to provide interpretable classification through a label-free Concept Bottleneck Model, without requiring explicit concept labels during training. We demonstrate PrivLEX's ability to identify personal data concepts that are present in images. We further analyse the sensitivity of such concepts as perceived by human annotators of image privacy datasets.

翻译：本文提出PrivLEX，一种新颖的图像隐私分类器，其决策基于法律定义的个人数据概念。PrivLEX是首个与法律概念对齐的可解释隐私分类器，它利用视觉语言模型（VLMs）的识别能力。PrivLEX依赖零样本VLM概念检测，通过无标签的概念瓶颈模型提供可解释分类，无需在训练期间使用显式的概念标签。我们证明了PrivLEX能够识别图像中存在的个人数据概念。进一步分析了人类标注者在图像隐私数据集中对此类概念的感知敏感度。