用大模型自动做公平性机器学习，降低门槛
FairAgent: Democratizing Fairness-Aware Machine Learning with LLM-Powered Agents
- 基于大模型自动分析数据偏见并处理预处理
- 在不损失性能前提下显著提升公平性
- 适合无公平性背景的开发者快速上手
训练公平且无偏的机器学习模型对高风险应用至关重要，但面临巨大挑战。有效缓解偏见需要掌握公平性定义、度量、数据预处理和机器学习技术等专业知识。同时，平衡模型性能与公平性要求，并妥善处理敏感属性，使公平性感知模型开发对多数从业者难以企及。为此，我们提出 FairAgent，一个由大语言模型驱动的自动化系统，显著简化了公平性感知模型的开发流程。FairAgent 通过自动分析数据中的潜在偏见，完成数据预处理与特征工程，并根据用户需求实施合适的偏见缓解策略，无需深厚技术背景。实验表明，FairAgent 在显著降低开发时间与专业要求的同时，实现了显著的性能提升，使公平性感知机器学习更易普及。
原文摘要 · Abstract (English)
Training fair and unbiased machine learning models is crucial for high-stakes applications, yet it presents significant challenges. Effective bias mitigation requires deep expertise in fairness definitions, metrics, data preprocessing, and machine learning techniques. In addition, the complex process of balancing model performance with fairness requirements while properly handling sensitive attributes makes fairness-aware model development inaccessible to many practitioners. To address these challenges, we introduce FairAgent, an LLM-powered automated system that significantly simplifies fairness-aware model development. FairAgent eliminates the need for deep technical expertise by automatically analyzing datasets for potential biases, handling data preprocessing and feature engineering, and implementing appropriate bias mitigation strategies based on user requirements. Our experiments demonstrate that FairAgent achieves significant performance improvements while significantly reducing development time and expertise requirements, making fairness-aware machine learning more accessible to practitioners.
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