Machine learning systems produce biased results towards certain demographic groups, known as the fairness problem. Recent approaches to tackle this problem learn a latent code (i.e., representation) through disentangled representation learning and then discard the latent code dimensions correlated with sensitive attributes (e.g., gender). Nevertheless, these approaches may suffer from incomplete disentanglement and overlook proxy attributes (proxies for sensitive attributes) when processing real-world data, especially for unstructured data, causing performance degradation in fairness and loss of useful information for downstream tasks. In this paper, we propose a novel fairness framework that performs debiasing with regard to both sensitive attributes and proxy attributes, which boosts the prediction performance of downstream task models without complete disentanglement. The main idea is to, first, leverage gradient-based explanation to find two model focuses, 1) one focus for predicting sensitive attributes and 2) the other focus for predicting downstream task labels, and second, use them to perturb the latent code that guides the training of downstream task models towards fairness and utility goals. We show empirically that our framework works with both disentangled and non-disentangled representation learning methods and achieves better fairness-accuracy trade-off on unstructured and structured datasets than previous state-of-the-art approaches.

翻译：机器学习系统在对待特定人群时会产生有偏的结果，即公平性问题。近期解决该问题的方法通过解耦表示学习学习潜在编码（即表示），然后丢弃与敏感属性（如性别）相关的潜在编码维度。然而，这些方法在处理真实世界数据（尤其是非结构化数据）时，可能因解耦不彻底且忽略代理属性（敏感属性的代理）而导致公平性性能下降和下游任务有用信息损失。本文提出一种新颖的公平性框架，该框架针对敏感属性和代理属性进行去偏处理，无需完全解耦即可提升下游任务模型的预测性能。其主要思想是：首先利用基于梯度的解释找到两个模型关注点——一个关注点用于预测敏感属性，另一个关注点用于预测下游任务标签；其次，利用这些关注点扰动指导下游任务模型训练的潜在编码，使其同时朝着公平性和效用目标优化。实验证明，该框架既适用于解耦表示学习方法也适用于非解耦表示学习方法，并且在非结构化和结构化数据集上均实现了比先前最优方法更优的公平性-准确性权衡。