负责任地评估机器学习 (ML) 模型不仅仅需要计算总体损失指标。在将模型投入生产环境之前，请务必审核训练数据并评估预测是否存在偏差。
本单元将介绍可能会在训练数据中显现出来的不同类型的人为偏差。然后，该文档提供了识别和减少偏见的策略，并以公平性为考量因素评估模型性能。
如未另行说明，那么本页面中的内容已根据知识共享署名 4.0 许可获得了许可，并且代码示例已根据 Apache 2.0 许可获得了许可。有关详情，请参阅 Google 开发者网站政策。Java 是 Oracle 和/或其关联公司的注册商标。
最后更新时间 (UTC)：2025-01-03。
[null,null,["最后更新时间 (UTC)：2025-01-03。"],[[["This module focuses on identifying and mitigating human biases that can negatively impact machine learning models."],["You'll learn how to proactively examine data for potential bias before model training and how to evaluate your model's predictions for fairness."],["The module explores various types of human biases that can unintentionally be replicated by machine learning algorithms, emphasizing responsible AI development."],["It builds upon foundational machine learning knowledge, including linear and logistic regression, classification, and handling numerical and categorical data."]]],[]]