- 了解组织信息治理政策
- 熟悉数据保护或隐私法规
- 接触过AI或机器学习概念会有所帮助
受众
- 信息治理专业人员
- 数据保护官和合规经理
- 数字化转型或IT治理负责人
人工智能和Machine Learning是快速发展的技术，对数据治理和组织合规性具有重要影响。
本课程为讲师指导的培训（线上或线下），面向中级水平的治理、隐私和合规专业人员，旨在帮助他们了解如何在人工智能和机器学习环境中实施稳健的信息治理实践。
通过本培训，参与者将能够：
- 理解人工智能和机器学习在信息治理背景下的核心概念。
- 识别组织中部署AI/ML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
- 互动讲座与讨论。
- 真实案例分析与框架评估。
- 小组活动与指导性文档解读。
课程定制选项
- 如需为本课程定制培训，请联系我们安排。
理解人工智能与Machine Learning
- 什么是人工智能，如何定义？
- Machine Learning作为人工智能的一个子集
- 人工智能的类型：弱人工智能、强人工智能、生成式人工智能、监督学习、无监督学习
人工智能在组织中的实践
- 人工智能/机器学习在业务功能中的当前应用
- 自动化、决策支持、客户服务和数据分析
- 在人力资源、财务、运营和合规中的使用案例
常见的Go治理挑战
- 与Data Protection原则的冲突
- 自动化决策中的合法性、公平性和透明度
- 准确性、数据最小化和存储限制
信息与Data Management的基础
- 人工智能背景下的信息和记录管理
- 元数据和审计跟踪的重要性
- 维护训练数据集的数据质量和完整性
应对信息Go治理挑战
- 为人工智能/机器学习管道设计治理控制措施
- 人类监督和可解释性
- 建立跨职能的治理团队
为人工智能/机器学习进行DPIAs
- DPIAs的法律要求和目的
- 评估拟议的人工智能/机器学习实施的步骤
- 记录风险评估、缓解措施和理由
Go治理框架与Risk Management
- 人工智能特定治理框架概述
- ISO、NIST、ICO和OECD的方法
- 风险登记和政策文档
文化、整合与相关框架
- 嵌入负责任的人工智能使用文化
- 将人工智能治理与网络安全、道德和ESG政策联系起来
- 持续改进和监控
总结与下一步
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