有时世界并不公平：使用自组织映射违反公平性
SOMtime the World Ain$'$t Fair: Violating Fairness Using Self-Organizing Maps
AI总结：
研究发现基于自组织映射的SOMtime方法，能让敏感属性在无监督嵌入中成为主导潜在轴，恢复与敏感属性对齐的排序，其嵌入分割产生人口统计学倾斜聚类，揭示“通过无意识实现公平”在表示层面失败，公平审计需扩展到无监督组件。
摘要：
人们普遍认为，当在训练中不考虑敏感属性时，无监督表示对这些属性是中立的。但本文表明这一假设是错误的。使用基于高容量自组织映射的拓扑保持表示方法SOMtime，研究发现年龄和收入等敏感属性会在纯无监督嵌入中成为主导潜在轴，即使明确从输入中排除。在两个大规模真实世界数据集上，SOMtime恢复了与被 withheld敏感属性对齐的单调排序，而PCA、UMAP、t-SNE和自动编码器表现较差。此外，SOMtime嵌入的无监督分割产生了人口统计学上倾斜的聚类。研究结果表明，“通过无意识实现公平”在序数敏感属性的表示层面上失败，公平性审计必须扩展到机器学习管道的无监督组件。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
