A critical concern in data-driven decision making is to build models whose
outcomes do not discriminate against some demographic groups, including gender,
ethnicity, or age. To ensure non-discrimination in learning tasks, knowledge of
the sensitive attributes is essential, while, in practice, these attributes may
not be available due to legal and ethical requirements. To address this
challenge, this paper studies a model that protects the privacy of the
individuals sensitive information while also allowing it to learn
non-discriminatory predictors. The method relies on the notion of differential
privacy and the use of Lagrangian duality to design neural networks that can
accommodate fairness constraints while guaranteeing the privacy of sensitive
attributes. The paper analyses the tension between accuracy, privacy, and
fairness and the experimental evaluation illustrates the benefits of the
proposed model on several prediction tasks.

这篇论文研究了如何使用差分隐私和拉格朗日对偶的方法来设计神经网络，以确保在预测中不出现性别、种族或年龄等偏见，同时保护个人敏感信息，对于保证准确性、隐私与公平之间的平衡关系问题进行了分析，并在多个预测任务上进行了实验验证。

拉格朗日对偶方法实现差分隐私和公平深度学习

Differentially Private and Fair Deep Learning: A Lagrangian Dual  Approach

Sensitive attributes such as race are rarely available to learners in real
world settings as their collection is often restricted by laws and regulations.
We give a scheme that allows individuals to release their sensitive information
privately while still allowing any downstream entity to learn
non-discriminatory predictors. We show how to adapt non-discriminatory learners
to work with privatized protected attributes giving theoretical guarantees on
performance. Finally, we highlight how the methodology could apply to learning
fair predictors in settings where protected attributes are only available for a
subset of the data.

本文研究通过个人隐私保护的方式，实现对受保护属性的学习，在使用中不产生歧视，同时在受保护属性只在数据子集中存在的情况下，实现公平预测模型的学习。