Counterfactual Fairness by Combining Factual and Counterfactual Predictions
This work addresses fairness concerns in high-stake domains like healthcare and hiring, but it is incremental as it builds on existing counterfactual fairness methods.
The paper tackles the trade-off between counterfactual fairness and predictive performance in machine learning models, proposing a method to convert an optimal predictor into a fair one without losing optimality, with experiments on synthetic and semi-synthetic datasets validating the approach.
In high-stake domains such as healthcare and hiring, the role of machine learning (ML) in decision-making raises significant fairness concerns. This work focuses on Counterfactual Fairness (CF), which posits that an ML model's outcome on any individual should remain unchanged if they had belonged to a different demographic group. Previous works have proposed methods that guarantee CF. Notwithstanding, their effects on the model's predictive performance remains largely unclear. To fill in this gap, we provide a theoretical study on the inherent trade-off between CF and predictive performance in a model-agnostic manner. We first propose a simple but effective method to cast an optimal but potentially unfair predictor into a fair one without losing the optimality. By analyzing its excess risk in order to achieve CF, we quantify this inherent trade-off. Further analysis on our method's performance with access to only incomplete causal knowledge is also conducted. Built upon it, we propose a performant algorithm that can be applied in such scenarios. Experiments on both synthetic and semi-synthetic datasets demonstrate the validity of our analysis and methods.