Unsupervised representations are widely assumed to be neutral with respect to sensitive attributes when those attributes are withheld from training. We show that this assumption is false. Using SOMtime, a topology-preserving representation method based on high-capacity Self-Organizing Maps, we demonstrate that sensitive attributes such as age and income emerge as dominant latent axes in purely unsupervised embeddings, even when explicitly excluded from the input. On two large-scale real-world datasets (the World Values Survey across five countries and the Census-Income dataset), SOMtime recovers monotonic orderings aligned with withheld sensitive attributes, achieving Spearman correlations of up to 0.85, whereas PCA and UMAP typically remain below 0.23 (with a single exception reaching 0.31), and against t-SNE and autoencoders which achieve at most 0.34. Furthermore, unsupervised segmentation of SOMtime embeddings produces demographically skewed clusters, demonstrating downstream fairness risks without any supervised task. These findings establish that \textit{fairness through unawareness} fails at the representation level for ordinal sensitive attributes and that fairness auditing must extend to unsupervised components of machine learning pipelines. We have made the code available at~ https://github.com/JosephBingham/SOMtime

翻译：人们普遍认为，当敏感属性在训练中被隐去时，无监督表示相对于这些属性是中立的。我们证明这一假设是错误的。利用SOMtime——一种基于高容量自组织映射的拓扑保持表示方法，我们证明了即使敏感属性（如年龄和收入）被明确排除在输入之外，它们仍会在纯粹的无监督嵌入中作为主导的潜在轴出现。在两个大规模真实世界数据集（涵盖五个国家的世界价值观调查和人口普查收入数据集）上，SOMtime恢复了与被隐去敏感属性对齐的单调排序，其斯皮尔曼相关系数最高可达0.85；而主成分分析和UMAP通常低于0.23（仅有一个例外达到0.31），t-SNE和自编码器最多仅达到0.34。此外，对SOMtime嵌入进行无监督分割会产生人口统计学上倾斜的聚类，这表明即使没有任何监督任务，下游公平性风险依然存在。这些发现证实，对于有序敏感属性而言，\textit{通过无知实现公平}在表示层面是失败的，并且公平性审计必须扩展到机器学习流程的无监督组件中。我们已将代码发布于~ https://github.com/JosephBingham/SOMtime