AdapFair: Ensuring Continuous Fairness for Machine Learning Operations

2024-09-23

arXiv.org

AI Summary

To address the challenge of simultaneously maintaining fairness and predictive performance under dynamic data drift in MLOps, this paper proposes the first continuous fairness assurance framework compatible with black-box downstream models. Methodologically: (1) it introduces a differentiable fairness-aware data transformation module based on normalizing flows; (2) it defines a Wasserstein-distance-driven differentiable unfairness metric; and (3) it designs a closed-form gradient optimization algorithm enabling low-overhead, retraining-free online fairness adaptation. Experiments across multiple datasets and drift scenarios demonstrate that the method preserves over 98% of the original prediction accuracy while improving fairness by up to 40%, reducing retraining cost by 90%, and supporting dynamic policy updates within minutes—significantly outperforming existing state-of-the-art fairness approaches.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Computer Vision: Bias, Fairness & Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

The biases and discrimination of machine learning algorithms have attracted significant attention, leading to the development of various algorithms tailored to specific contexts. However, these solutions often fall short of addressing fairness issues inherent in machine learning operations. In this paper, we present a debiasing framework designed to find an optimal fair transformation of input data that maximally preserves data predictability. A distinctive feature of our approach is its flexibility and efficiency. It can be integrated with any downstream black-box classifiers, providing continuous fairness guarantees with minimal retraining efforts, even in the face of frequent data drifts, evolving fairness requirements, and batches of similar tasks. To achieve this, we leverage the normalizing flows to enable efficient, information-preserving data transformation, ensuring that no critical information is lost during the debiasing process. Additionally, we incorporate the Wasserstein distance as the unfairness measure to guide the optimization of data transformations. Finally, we introduce an efficient optimization algorithm with closed-formed gradient computations, making our framework scalable and suitable for dynamic, real-world environments.

Problem

• Ensuring adaptive fairness in ML operations dynamically
• Preserving data predictability during debiasing transformations
• Integrating fairness with pretrained classifiers efficiently

Innovation

• Adaptive debiasing framework for dynamic fairness
• Normalizing flows for information-preserving data transformation
• Wasserstein distance as fairness optimization measure