Practical Bias Mitigation through Proxy Sensitive Attribute Label Generation

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.