Balancing Fairness, Privacy, and Accuracy: A Multitask Adversarial Framework for Centralized Data-Driven Systems

2026-05-23

AI Summary

This work addresses the inherent tension among fairness, privacy preservation, and model accuracy in centralized data-driven systems, which are typically difficult to optimize jointly. The paper introduces, for the first time, a unified formulation of these three objectives as a joint optimization problem and proposes an end-to-end trainable multi-task adversarial framework. This framework leverages information bottlenecks and adversarial learning to construct latent representations that obscure sensitive attributes while preserving task-relevant information. A dynamically weighted joint loss function is designed to simultaneously enforce privacy and fairness constraints without compromising predictive performance. Experimental results across multiple benchmark datasets demonstrate that the proposed method significantly outperforms existing approaches, achieving high accuracy even under stringent privacy and fairness requirements.

Abstract

The integration of fairness and privacy in centralized data-driven applications is critical, especially as these systems increasingly influence sectors with significant societal impact. Current methods rarely address privacy, fairness, and accuracy together, which can potentially compromise ethical standards and privacy regulations. However, balancing these three objectives is quite challenging since each of objective often imposes conflicting requirements on the design and training of models, making it difficult to optimize one without compromising the others. This paper introduces a novel multitask adversarial model that treats fairness and privacy as integral objectives rather than afterthoughts, and learns a latent representation that hides sensitive attributes while preserving essential task-related information. Our approach dynamically balances fairness with accuracy and privacy through an optimized cost function with minimal performance loss even under strict conditions. Extensive testing on diverse datasets shows the ability of our model to achieve high standards of fairness and privacy without significant sacrifice to accuracy. Benchmarking against state-of-the-art privacy and fairness standards shows that our method enhances the robustness of privacy, fairness, and accuracy optimization, proving its adaptability across various datasets.

Problem

• fairness
• privacy
• accuracy
• centralized data-driven systems
• conflicting objectives

Innovation

• multitask adversarial learning
• fairness-privacy-accuracy trade-off
• latent representation
• centralized data-driven systems
• ethical machine learning