Practical Bias Mitigation through Proxy Sensitive Attribute Label
  Generation
Bhushan Chaudhary Anubha Pandey Deepak Bhatt Darshika Tiwari
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.