Counterfactual Fairness through Transforming Data Orthogonal to Bias
This addresses fairness issues in machine learning for applications where biased decisions affect different groups, though it is incremental as it builds on existing counterfactual fairness research.
The paper tackles the problem of biased decision-making in machine learning models by proposing a data pre-processing algorithm, Orthogonal to Bias (OB), to eliminate the influence of continuous sensitive variables for counterfactual fairness, showing it effectively promotes fairer outcomes without compromising accuracy in empirical evaluations on simulated and real-world datasets.
Machine learning models have shown exceptional prowess in solving complex issues across various domains. However, these models can sometimes exhibit biased decision-making, resulting in unequal treatment of different groups. Despite substantial research on counterfactual fairness, methods to reduce the impact of multivariate and continuous sensitive variables on decision-making outcomes are still underdeveloped. We propose a novel data pre-processing algorithm, Orthogonal to Bias (OB), which is designed to eliminate the influence of a group of continuous sensitive variables, thus promoting counterfactual fairness in machine learning applications. Our approach, based on the assumption of a jointly normal distribution within a structural causal model (SCM), demonstrates that counterfactual fairness can be achieved by ensuring the data is orthogonal to the observed sensitive variables. The OB algorithm is model-agnostic, making it applicable to a wide range of machine learning models and tasks. Additionally, it includes a sparse variant to improve numerical stability through regularization. Empirical evaluations on both simulated and real-world datasets, encompassing settings with both discrete and continuous sensitive variables, show that our methodology effectively promotes fairer outcomes without compromising accuracy.