Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach
This addresses the problem of balancing fairness, privacy, and accuracy in data-driven decision-making for applications like hiring or lending, though it is incremental as it builds on existing differential privacy and fairness techniques.
The paper tackles the challenge of building non-discriminatory machine learning models when sensitive attributes are unavailable due to privacy concerns, by proposing a method that uses differential privacy and Lagrangian duality to ensure fairness while protecting sensitive information, with experimental results showing benefits on prediction tasks.
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.