BiasGuard：守护机器学习生产系统中的公平性
BiasGuard: Guardrailing Fairness in Machine Learning Production Systems
AI总结：
本文提出BiasGuard，利用CTGAN驱动的测试时增强在不重训模型的情况下修正已部署ML系统输出，实验显示公平性提升31%且准确率仅降0.09%。
摘要：
随着机器学习（ML）系统日益影响招聘、金融风险评估和刑事司法等关键领域，由于其潜在的负面影响，确保公平性的必要性愈发迫切。尽管许多机器学习公平性研究聚焦于改进训练数据和流程，但对已部署系统输出的处理却关注较少。本文介绍了“BiasGuard”，这是一种旨在充当生产级机器学习系统公平性护栏的新颖方法。BiasGuard利用由条件生成对抗网络（CTGAN，一种前沿生成式AI模型）驱动的测试时增强（TTA），合成以反转后的受保护属性值为条件的数据样本，从而促进不同群体间的公平结果。该方法旨在为特权群体和非特权群体提供平等机会，同时在无需重新训练的情况下显著提升已部署系统的公平性指标。我们在多个数据集上的综合实验分析表明，与未采取缓解措施的基准相比，BiasGuard将公平性提升了31%，而准确率仅下降0.09%。此外，BiasGuard在改善公平性方面优于现有的后处理方法，这使其成为在模型重新训练不可行时防范偏见的有效工具。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
