Counterfactual Fairness through Transforming Data Orthogonal to Bias
Shuyi Chen Shixiang Zhu
Machine learning models have shown exceptional prowess in solving complex
issues across various domains. Nonetheless, these models can sometimes exhibit
biased decision-making, leading to disparities in treatment across different
groups. Despite the extensive research on fairness, the nuanced effects of
multivariate and continuous sensitive variables on decision-making outcomes
remain insufficiently studied. We introduce a novel data pre-processing
algorithm, Orthogonal to Bias (OB), designed to remove the influence of a group
of continuous sensitive variables, thereby facilitating counterfactual fairness
in machine learning applications. Our approach is grounded in the assumption of
a jointly normal distribution within a structural causal model (SCM), proving
that counterfactual fairness can be achieved by ensuring the data is
uncorrelated with sensitive variables. The OB algorithm is model-agnostic,
catering to a wide array of machine learning models and tasks, and includes a
sparse variant to enhance numerical stability through regularization. Through
empirical evaluation on simulated and real-world datasets - including the adult
income and the COMPAS recidivism datasets - our methodology demonstrates its
capacity to enable fairer outcomes without compromising accuracy.