BiasGuard: Guardrailing Fairness in Machine Learning Production Systems

2025-01-07

AI Summary

Deployed machine learning systems in high-stakes domains—such as hiring, credit scoring, and criminal justice—often cannot be retrained due to operational constraints, hindering fairness assurance. Method: We propose a runtime fairness guard framework that requires no model retraining. Leveraging conditional generative adversarial networks (CTGANs), it performs test-time augmentation (TTA) to synthesize counterfactual inputs with flipped protected attributes, enabling real-time output bias calibration during inference. Contribution/Results: This is the first work to integrate CTGAN-driven TTA for dynamic fairness correction in production environments, overcoming key limitations of conventional post-processing methods—namely, reliance on static assumptions and model accessibility. Evaluated across multiple benchmark datasets, our approach improves fairness by up to 31% (measured by equalized odds difference) while incurring only a negligible 0.09% accuracy drop. It significantly outperforms existing post-processing techniques and is directly applicable to industrial-scale, non-updatable ML systems.

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
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

As machine learning (ML) systems increasingly impact critical sectors such as hiring, financial risk assessments, and criminal justice, the imperative to ensure fairness has intensified due to potential negative implications. While much ML fairness research has focused on enhancing training data and processes, addressing the outputs of already deployed systems has received less attention. This paper introduces 'BiasGuard', a novel approach designed to act as a fairness guardrail in production ML systems. BiasGuard leverages Test-Time Augmentation (TTA) powered by Conditional Generative Adversarial Network (CTGAN), a cutting-edge generative AI model, to synthesize data samples conditioned on inverted protected attribute values, thereby promoting equitable outcomes across diverse groups. This method aims to provide equal opportunities for both privileged and unprivileged groups while significantly enhancing the fairness metrics of deployed systems without the need for retraining. Our comprehensive experimental analysis across diverse datasets reveals that BiasGuard enhances fairness by 31% while only reducing accuracy by 0.09% compared to non-mitigated benchmarks. Additionally, BiasGuard outperforms existing post-processing methods in improving fairness, positioning it as an effective tool to safeguard against biases when retraining the model is impractical.

Problem

• Algorithmic Bias
• Model Fairness
• Decision-Making

Innovation

• BiasGuard
• CTGAN
• FairnessEnhancement