对比两大开源工具箱，实测如何用多阶段方法减少视觉与语言模型偏见。
Analyzing Fairness of Computer Vision and Natural Language Processing Models
- 分别使用微软Fairlearn和IBM AIF360，在预处理、训练中、后处理阶段单独或串行应用去偏算法。
- 串行应用多阶段算法能更有效降低偏见，同时保持模型性能不下降。
- 适合关注模型公平性的开发者、研究人员及需要合规部署AI系统的团队。
机器学习算法在医疗、金融、教育和执法等领域决策中发挥关键作用，但其公平性与偏见问题引发重大伦理与社会挑战。本研究采用微软Fairlearn与IBM AIF360两大主流公平性工具库，评估并缓解计算机视觉（CV）与自然语言处理（NLP）模型在非结构化数据上的偏见。研究聚焦于在机器学习生命周期的预处理、训练中、后处理三个阶段中，单独或串行应用去偏算法的性能表现。结果表明，部分串行应用策略能更有效地降低偏见，同时维持模型性能。实验基于Kaggle公开数据集，提供真实场景下的公平性评估依据。
原文摘要 · Abstract (English)
Machine learning (ML) algorithms play a critical role in decision-making across various domains, such as healthcare, finance, education, and law enforcement. However, concerns about fairness and bias in these systems have raised significant ethical and social challenges. To address these challenges, this research utilizes two prominent fairness libraries, Fairlearn by Microsoft and AIF360 by IBM. These libraries offer comprehensive frameworks for fairness analysis, providing tools to evaluate fairness metrics, visualize results, and implement bias mitigation algorithms. The study focuses on assessing and mitigating biases for unstructured datasets using Computer Vision (CV) and Natural Language Processing (NLP) models. The primary objective is to present a comparative analysis of the performance of mitigation algorithms from the two fairness libraries. This analysis involves applying the algorithms individually, one at a time, in one of the stages of the ML lifecycle, pre-processing, in-processing, or post-processing, as well as sequentially across more than one stage. The results reveal that some sequential applications improve the performance of mitigation algorithms by effectively reducing bias while maintaining the model's performance. Publicly available datasets from Kaggle were chosen for this research, providing a practical context for evaluating fairness in real-world machine learning workflows.
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