Thinking Outside the Box: Orthogonal Approach to Equalizing Protected
  Attributes
Jiahui Liu Xiaohao Cai Mahesan Niranjan
There is growing concern that the potential of black box AI may exacerbate
health-related disparities and biases such as gender and ethnicity in clinical
decision-making. Biased decisions can arise from data availability and
collection processes, as well as from the underlying confounding effects of the
protected attributes themselves. This work proposes a machine learning-based
orthogonal approach aiming to analyze and suppress the effect of the confounder
through discriminant dimensionality reduction and orthogonalization of the
protected attributes against the primary attribute information. By doing so,
the impact of the protected attributes on disease diagnosis can be realized,
undesirable feature correlations can be mitigated, and the model prediction
performance can be enhanced.