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Lead AI Governance, Policy, and Continuous Compliance

位教师：LearnQuest Network

包含在中

了解更多

3个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

3 小时完成

灵活的计划

自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

3 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Design KPI frameworks to measure, benchmark, and continuously improve AI governance maturity across model iterations.
Build cross-functional governance coalitions that align engineering, legal, and compliance teams around accountable AI practices
Apply regulatory foresight and policy mapping to future-proof AI systems against evolving global standards and AI regulations.

您将获得的技能

您将学习的工具

Dashboard

要了解的详细信息

可分享的证书

添加到您的领英档案

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有3个模块

This course equips data scientists, ML engineers, and AI risk professionals with the strategic tools to sustain responsible AI programs at scale. You will build KPI frameworks to measure and benchmark governance maturity, design feedback loops that strengthen compliance over successive model iterations, and create executive-level dashboards that make governance performance visible to senior stakeholders.

You will also develop the collaboration skills to bridge engineering, legal, and compliance teams where you will be establishing shared accountability structures, ethics committees, and documentation workflows that embed responsible AI into your organization's culture. In the final module, you will apply regulatory foresight techniques to anticipate emerging standards across the EU AI Act, NIST RMF, and global jurisdictions, and build adaptive compliance policies that evolve with the landscape, not behind it. Learners with experience in ML, data science, or AI project management, and a working familiarity with compliance or risk concepts, will be best positioned to apply these frameworks immediately in their organizations.

单元详情

Governance policies only prove their value when you can measure whether they work. In this module, you move from designing AI governance frameworks to quantifying their effectiveness. You will learn how to define key performance indicators that capture risk coverage, process adherence, and harm outcomes across your AI portfolio, then use maturity benchmarks drawn from frameworks like the NIST AI Risk Management Framework and ISO 42001 to validate policy performance against internal and external standards. You will also build structured feedback loops and monitoring dashboards that keep governance current as models, regulations, and business conditions change. By the end of this module, you will be able to design a governance measurement system that tracks, validates, and continuously improves responsible AI performance.

涵盖的内容

11个视频2篇阅读材料1个作业

11个视频总计44分钟

Welcome to Course 32分钟
Measure and Validate Governance Effectiveness2分钟
Spot the Dashboard That Measures Nothing About Governance3分钟
Distinguish What Governance Metrics Actually Measure5分钟
Build a Governance KPI Dashboard from Objectives to Indicators6分钟
Prioritize Governance KPIs Where Regulatory Exposure Is Greatest5分钟
Spot the Governance Program That Stopped Learning 3分钟
Equip Your Governance System to Learn from Itself 6分钟
Build the Feedback Loop from Monitoring Data to Governance Change 6分钟
Diagnose Where Your Governance Feedback Loop is Broken 5分钟
Module Summary1分钟

2篇阅读材料总计20分钟

Course Syllabus10分钟
From Illusion to Insight: Measuring What Truly Matters in AI Governance10分钟

1个作业总计10分钟

Measure and Validate Governance Effectiveness10分钟

This module equips you with the governance, accountability, and compliance skills needed to lead responsible AI programs in complex, regulated environments. You will explore how to define and measure governance performance through meaningful metrics and key performance indicators, build continuous improvement cycles that keep governance in step with evolving models and regulations, and construct cross-functional coalitions that align technical, legal, and business teams around shared accountability. You will also develop the regulatory foresight needed to adapt compliance policies across overlapping frameworks—including the EU AI Act, NIST AI Risk Management Framework, and ISO 42001—and learn to benchmark your organization's AI maturity against global standards. By the end of this module, you will be able to design defensible, audit-ready governance structures and translate regulatory obligations into actionable operational controls.