提出兼顾公平与性能的机器学习公平性方法
Counterfactual Fairness by Combining Factual and Counterfactual Predictions
- 将最优预测器转化为公平模型而不损失最优性
- 量化了公平性与预测性能间的内在权衡关系
- 适用于因果知识不完整场景，适合高风险决策领域
在医疗、招聘等高风险领域，机器学习决策引发重大公平性问题。本文聚焦反事实公平性（CF），即个体在不同族裔群体下应获得相同预测结果。已有方法虽能保证CF，但对预测性能的影响尚不明确。为此，本文从模型无关角度理论分析了CF与预测性能间的内在权衡。提出一种简单有效的方法，可在不损失最优性的前提下将最优但不公平的预测器转为公平模型。通过分析其达到CF所需的额外风险，量化了该权衡。进一步研究了在仅有不完整因果知识下的方法表现，并据此提出高效算法。在合成与半合成数据集上的实验验证了分析与方法的有效性。
原文摘要 · Abstract (English)
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.
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