Course Code: sbmftm
Duration: 14 hours
Prerequisites:
  • 了解机器学习模型与训练流程
  • 具备微调与LLMs的实务经验
  • 熟悉Python与NLP概念

目标受众

  • AI合规团队
  • ML工程师
Overview:

Safety and Bias Mitigation in Fine-Tuned Models 随著人工智慧在各行业的决策中日益深入，以及监管标准的不断演进，已成为一个日益关注的问题。

这项由讲师指导的培训（线上或线下）针对中级机器学习工程师和人工智慧合规专业人员，旨在帮助他们识别、评估并减少微调语言模型中的安全风险和偏见。

培训结束后，参与者将能够：

  • 了解安全人工智慧系统的伦理和监管背景。
  • 识别并评估微调模型中的常见偏见形式。
  • 在训练期间及之后应用偏见缓解技术。
  • 设计并审核模型，确保其安全性、透明性和公平性。

课程形式

  • 互动式讲座与讨论。
  • 大量练习与实践。
  • 在即时实验室环境中进行实作。

课程定制选项

  • 如需为本课程定制培训，请联系我们安排。
Course Outline:

安全与公平AI的基础

  • 关键概念：安全性、偏见、公平性、透明度
  • 偏见类型：数据集偏见、代表性偏见、算法偏见
  • 监管框架概述（欧盟AI法案、GDPR等）

微调模型中的偏见

  • 微调如何引入或放大偏见
  • 案例研究与现实中的失败案例
  • 识别数据集和模型预测中的偏见

偏见缓解技术

  • 数据层面策略（重新平衡、数据增强）
  • 训练中策略（正则化、对抗性去偏见）
  • 后处理策略（输出过滤、校准）

模型安全与稳健性

  • 检测不安全或有害的输出
  • 处理对抗性输入
  • 红队演练与压力测试微调模型

审计与监控AI系统

  • 偏见与公平性评估指标（如人口统计平等）
  • 可解释性工具与透明度框架
  • 持续监控与治理实践

工具包与实践操作

  • 使用开源库（如Fairlearn、Transformers、CheckList）
  • 实践操作：检测与缓解微调模型中的偏见
  • 通过提示设计与约束生成安全输出

企业Use Case与合规准备

  • 在LLM工作流程中整合安全性的最佳实践
  • Documentation与模型卡片用于合规
  • 准备审计与外部审查

总结与下一步

Sites Published:

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