Course Code: aimligig
Duration: 7 hours
Prerequisites:
  • 了解組織的信息治理政策
  • 熟悉數據保護或隱私法規
  • 對AI或機器學習概念有所了解將有所幫助

目標受眾

  • 信息治理專業人員
  • 數據保護官和合規經理
  • 數字轉型或IT治理負責人
Overview:

AI和Machine Learning是快速发展的技术,对数据治理和组织合规性具有重大影响。

本课程为讲师主导的培训(线上或线下),面向中级水平的治理、隐私和合规专业人士,旨在帮助他们在使用AI和机器学习的环境中实施稳健的信息治理实践。

培训结束后,学员将能够:

  • 理解AI和机器学习的核心概念在信息治理中的应用。
  • 识别组织中AI/ML部署的典型领域及相关的信息治理风险。
  • 评估AI/ML如何与Data Protection原则、记录管理和数据质量相互作用。
  • 将DPIAs、治理框架和风险缓解方法应用于AI/ML项目。

课程形式

  • 互动式讲座与讨论。
  • 真实案例分析与框架解读。
  • 小组活动与指导性文档演练。

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:

理解人工智慧與Machine Learning

  • 什麼是人工智慧?如何定義?
  • Machine Learning作為人工智慧的子集
  • 人工智慧的類型:弱人工智慧、強人工智慧、生成式人工智慧、監督式學習、非監督式學習

人工智慧在組織中的實踐

  • 人工智慧/機器學習目前在業務功能中的應用
  • 自動化、決策支援、客戶服務與分析
  • 在人力資源、財務、運營與合規中的使用案例

常見的Go治理挑戰

  • 與Data Protection原則的衝突
  • 自動化決策中的合法性、公平性與透明度
  • 準確性、數據最小化與存儲限制

信息與Data Management的基礎

  • 人工智慧情境中的信息與記錄管理
  • 元數據與審計追蹤的重要性
  • 維護訓練數據集的數據質量與完整性

應對信息Go治理挑戰

  • 為人工智慧/機器學習管道設計治理控制
  • 人類監督與可解釋性
  • 建立跨職能治理團隊

為人工智慧/機器學習進行DPIAs

  • DPIAs的法律要求與目的
  • 評估擬議人工智慧/機器學習實施的步驟
  • 記錄風險評估、緩解措施與理由

Go治理框架與Risk Management

  • 人工智慧專用治理框架概述
  • ISO、NIST、ICO與OECD的方法
  • 風險登記與政策文件

文化、整合與相關框架

  • 嵌入負責任使用人工智慧的文化
  • 將人工智慧治理與網路安全、倫理與ESG政策相連結
  • 持續改進與監控

總結與下一步

Sites Published:

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