What background knowledge is necessary?

A working understanding of data management concepts, familiarity with AI systems, and basic knowledge of data ethics will help you get the most from this course.

What topics are covered in this course?

The course covers data governance principles, traceable data lineage, content authenticity frameworks, ethics-by-design methodologies, governance automation, and regulatory compliance for AI systems.

What is the learning experience of this course?

The course combines theoretical instructions with practical application through hands-on labs, demos, case studies, and expert-led dialogues. You'll work on real-world governance challenges and produce documentation and frameworks applicable to your organization.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Data Lineage & Ethical Frameworks for Responsible AI

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Data Lineage & Ethical Frameworks for Responsible AI

本课程是 Modern Data Strategy for Enterprise Generative AI 专项课程的一部分

位教师：David Drummond

包含在中

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3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

7 小时完成

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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。

中级等级

推荐体验

7 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Translate business requirements into lineage models and control points
Capture and visualize lineage from source data to deployed models, build datasheets/model cards that make AI decisions explainable
Apply NIST AI RMF‑aligned assessments and define mitigations
Conduct privacy impact assessments and implement consent/retention controls & produce audit‑ready evidence packs and governance dashboards

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

积累特定领域的专业知识

本课程是 Modern Data Strategy for Enterprise Generative AI 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有3个模块

Transform how your organization builds trust in AI. Learn to design end‑to‑end data lineage and ethical governance frameworks that make AI explainable, auditable, and compliant—without slowing innovation.

In this hands‑on course, you’ll capture provenance from source to model, manage metadata and documentation (datasheets, model cards), and operationalize risk controls aligned to industry guidance. Practice integrating lineage with the AI lifecycle—ingestion, training, deployment, monitoring—and implement privacy, fairness, and quality assurance guardrails. You’ll produce audit‑ready evidence packs, dashboards, and review artifacts that withstand scrutiny from leadership, regulators, and clients. By the end, you’ll design governed AI workflows, evaluate risks and mitigations, and institutionalize ethical review gates for reliable, accountable outcomes.

Explore the core principles of data governance and ethical AI. Learn how frameworks like C2PA and tools like DuckLake help verify content authenticity and detect misinformation. Understand global governance perspectives and how to embed trust into AI systems.

涵盖的内容

8个视频3篇阅读材料3个作业

8个视频总计35分钟

Course Introduction2分钟
What is Data Governance4分钟
Frameworks of Data Governance6分钟
DuckLake for Data Governance3分钟
Tagging AI-Generated Content 4分钟
C2PA Explained: Digital Content Provenance 5分钟
Misinformation Detection Usecase 4分钟
Building trust in AI systems4分钟

3篇阅读材料总计17分钟

Course syllabus5分钟
Disclaimer2分钟
World Economic Forum – AI Governance Framework10分钟

3个作业总计80分钟

Understanding Data Governance and Its Frameworks20分钟
Content Integrity & Oversight30分钟
Governance Foundations and Content Authenticity30分钟

Dive into the architecture of data pipelines and learn how to build them with governance in mind. Use tools like OpenLineage to visualize, monitor, and trace data across its lifecycle—ensuring transparency and compliance.

涵盖的内容

6个视频1篇阅读材料3个作业2个非评分实验室

6个视频总计33分钟

Introduction to Data Pipelines5分钟
Building Governance into Data Pipelines3分钟
Visualizing a Data Pipeline8分钟
Visualizing Data Lineage5分钟
Lineage as Pipeline Intelligence4分钟
Implementing Data Lineage Tracking4分钟

1篇阅读材料总计10分钟

Importance of Data Pipelines10分钟

3个作业总计80分钟

Designing and Governing Modern Data Pipelines20分钟
Pipeline Tracking & Lineage30分钟
Data Pipelines and Lineage30分钟

2个非评分实验室总计90分钟

Visualizing and Monitoring Data Flows45分钟
Data Lineage Tracking45分钟

Design AI systems that are both powerful and principled. Learn ethics-by-design workflows, governance automation, and advanced compliance strategies to future-proof your AI initiatives.

涵盖的内容

10个视频3个作业

10个视频总计48分钟

Principles of Responsible AI 4分钟
Data Governance and RAI5分钟
Data Governance and Ethical AI4分钟
Designing Ethics-by-Design Workflows4分钟
Implementing Governance Automation with Machine Learning3分钟
Evaluating Governance Effectiveness 5分钟
Security in Governance Frameworks4分钟
Advanced Governance Patterns4分钟
Future Trends in AI & Data Strategy 5分钟
Tying it all back5分钟

3个作业总计80分钟

Principles and Practices of Responsible AI through Data Governance20分钟
Ethical AI & Governance in Action30分钟
Responsible AI & Applied Governance30分钟

获得职业证书

将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。

位教师

David Drummond

Fractal Analytics

3 门课程1,205 名学生

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Fractal Analytics

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常见问题

This course provides practical frameworks and techniques for implementing ethical, traceable, and compliant data practices for AI systems. It's important because organizations face increasing regulatory scrutiny and public expectations regarding AI transparency and ethics.

This course is designed for professionals who need to ensure AI systems meet ethical standards and regulatory requirements while maintaining data traceability and governance.

You'll be able to design governance frameworks for AI systems, implement data lineage tracking, create ethics-by-design workflows, and establish content authenticity verification systems. These skills enable you to build responsible AI systems that maintain trust and compliance.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.