Differentially Private Fair Binary Classifications
This work addresses the problem of ensuring both privacy and fairness in machine learning models, which is crucial for applications like credit scoring or employment screening, though it appears incremental as it builds on existing decoupling techniques.
The paper tackled binary classification under both differential privacy and fairness constraints by proposing an algorithm that decouples classifiers from different demographic groups to ensure statistical parity, then refined it for privacy. Empirical results on Adult and Credit Card datasets showed it outperformed state-of-the-art methods in fairness while maintaining privacy and utility levels.
In this work, we investigate binary classification under the constraints of both differential privacy and fairness. We first propose an algorithm based on the decoupling technique for learning a classifier with only fairness guarantee. This algorithm takes in classifiers trained on different demographic groups and generates a single classifier satisfying statistical parity. We then refine this algorithm to incorporate differential privacy. The performance of the final algorithm is rigorously examined in terms of privacy, fairness, and utility guarantees. Empirical evaluations conducted on the Adult and Credit Card datasets illustrate that our algorithm outperforms the state-of-the-art in terms of fairness guarantees, while maintaining the same level of privacy and utility.