Addressing bias in the trained machine learning system often requires access to sensitive attributes. In practice, these attributes are not available either due to legal and policy regulations or data unavailability for a given demographic. Existing bias mitigation algorithms are limited in their applicability to real-world scenarios as they require access to sensitive attributes to achieve fairness. In this research work, we aim to address this bottleneck through our proposed unsupervised proxy-sensitive attribute label generation technique. Towards this end, we propose a two-stage approach of unsupervised embedding generation followed by clustering to obtain proxy-sensitive labels. The efficacy of our work relies on the assumption that bias propagates through non-sensitive attributes that are correlated to the sensitive attributes and, when mapped to the high dimensional latent space, produces clusters of different demographic groups that exist in the data. Experimental results demonstrate that bias mitigation using existing algorithms such as Fair Mixup and Adversarial Debiasing yields comparable results on derived proxy labels when compared against using true sensitive attributes.

通过提出的无监督代理敏感属性标签生成技术来解决在机器学习系统中的偏见问题，该方法包括无监督嵌入生成和聚类两个阶段，其有效性依赖于偏见通过与敏感属性相关的非敏感属性传播，并在高维潜在空间中映射为不同人口群体的聚类。实验结果表明，使用现有算法进行偏见缓解可以得到与真实敏感属性相比可比较的结果。

通过生成代理敏感属性标签实现实际偏见缓解