Machine learningTrustworthy ML
公平感知机器学习
公平感知机器学习 (Fairness-Aware Machine Learning) 是一类技术，用于训练、约束或后处理预测模型，使其在种族、性别或年龄等受保护的人口统计群体之间的错误率或结果更加公平。由 Moritz Hardt、Eric Price 和 Nati Srebro 在其 2016 年的 NeurIPS 会议论文中正式确立了“均等赔率”(equalized odds) 和“机会均等”(equality of opportunity) 的基本框架，为非歧视性分类器建立了严格的统计标准。
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来源
- Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. Advances in Neural Information Processing Systems, 29. link ↗
如何引用本页
ScholarGate. (2026, June 2). Fairness-Aware Machine Learning. ScholarGate. https://scholargate.app/zh/machine-learning/fairness-aware-ml
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