[论文解读] You Can Still Achieve Fairness Without Sensitive Attributes: Exploring Biases in Non-Sensitive Features.

Tianxiang Zhao, Enyan Dai+2 more|arXiv (Cornell University)|Apr 29, 2021

Ethics and Social Impacts of AI参考文献 34被引用 8

一句话总结

本文提出了一种新颖的框架，在不依赖敏感属性的情况下实现公平机器学习，通过利用与敏感属性高度相关的非敏感特征。通过最小化这些代理特征与模型预测之间的相关性，同时动态调整其贡献权重，该方法在真实世界数据集上实现了高公平性和高准确性。

ABSTRACT

Though machine learning models are achieving great success, ex-tensive studies have exposed their disadvantage of inheriting latent discrimination and societal bias from the training data, which hinders their adoption on high-state applications. Thus, many efforts have been taken for developing fair machine learning models. Most of them require that sensitive attributes are available during training to learn fair models. However, in many real-world applications, it is usually infeasible to obtain the sensitive attribute due to privacy or legal issues, which challenges existing fair classifiers. Though the sensitive attribute of each data sample is unknown, we observe that there are usually some non-sensitive features in the training data that are highly correlated with sensitive attributes, which can be used to alleviate the bias. Therefore, in this paper, we study a novel problem of exploring features that are highly correlated with sensitive attributes for learning fair and accurate classifier without sensitive attributes. We theoretically show that by minimizing the correlation between these related features and model prediction, we can learn a fair classifier. Based on this motivation, we propose a novel framework which simultaneously uses these related features for accurate prediction and regularizing the model to be fair. In addition, the model can dynamically adjust the importance weight of each related feature to balance the contribution of the feature on model classification and fairness. Experimental results on real-world datasets demonstrate the effectiveness of the proposed model for learning fair models with high classification accuracy.

研究动机与目标

• 解决在因隐私或法律限制而无法获取敏感属性时，训练公平机器学习模型的挑战。
• 识别与敏感属性高度相关的非敏感特征，作为公平性缓解的代理。
• 开发一种框架，通过基于这些代理特征正则化模型预测，同时确保高分类准确性和公平性。
• 动态调整代理特征的重要性权重，以平衡其对准确性和公平性的影响。

提出的方法

• 该框架在训练数据中识别与敏感属性高度相关的非敏感特征，并将其用作代理。
• 它构建了一个正则化目标函数，以最小化预测结果与这些代理特征之间的相关性，从而减少偏差。
• 一种动态加权机制调整每个代理特征的贡献，以平衡分类性能与公平性。
• 该方法将准确预测与公平性正则化整合到一个端到端的学习过程中。
• 该框架使用标准优化技术进行训练，损失函数结合了交叉熵损失和公平性正则化项。

实验结果

研究问题

• RQ1 与敏感属性相关的非敏感特征能否被有效用于提升机器学习模型的公平性？
• RQ2 当无法获取敏感属性时，模型如何在分类准确性与公平性之间实现权衡？
• RQ3 动态加权代理特征对公平性与准确性表现有何影响？
• RQ4 与现有无需敏感属性的公平学习方法相比，所提出的框架在减少偏差方面的有效性如何？

主要发现

• 所提出的框架即使在无法访问敏感属性的情况下，也能实现高水平的公平性表现，且在公平性度量上优于基线方法。
• 使用与敏感属性相关的代理特征显著降低了模型偏差，同时保持了高预测准确性。
• 对代理特征的动态加权提升了模型在多样化数据集上平衡公平性与准确性的能力。
• 在真实世界数据集上的实证结果证实，最小化预测结果与代理特征之间的相关性，可带来更公平的模型结果。