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

This course is part of Modern Data Strategy for Enterprise Generative AI Specialization

Instructors: David Drummond

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

8 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

8 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

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

Skills you'll gain

Tools you'll learn

AI Workflows

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

9 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the Modern Data Strategy for Enterprise Generative AI Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

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.

What's included

8 videos3 readings3 assignments

8 videosTotal 36 minutes

Course Introduction2 minutes
What is Data Governance5 minutes
Frameworks of Data Governance6 minutes
DuckLake for Data Governance4 minutes
Tagging AI-Generated Content 4 minutes
C2PA Explained: Digital Content Provenance 5 minutes
Misinformation Detection Usecase 5 minutes
Building trust in AI systems5 minutes

3 readingsTotal 17 minutes

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

3 assignmentsTotal 80 minutes

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

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.

What's included

6 videos1 reading3 assignments2 ungraded labs

6 videosTotal 33 minutes

Introduction to Data Pipelines5 minutes
Building Governance into Data Pipelines4 minutes
Visualizing a Data Pipeline9 minutes
Visualizing Data Lineage6 minutes
Lineage as Pipeline Intelligence5 minutes
Implementing Data Lineage Tracking5 minutes

1 readingTotal 10 minutes

Importance of Data Pipelines10 minutes

3 assignmentsTotal 80 minutes

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

2 ungraded labsTotal 90 minutes

Visualizing and Monitoring Data Flows45 minutes
Data Lineage Tracking45 minutes

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.

What's included

10 videos3 assignments

10 videosTotal 49 minutes

Principles of Responsible AI 5 minutes
Data Governance and RAI6 minutes
Data Governance and Ethical AI4 minutes
Designing Ethics-by-Design Workflows4 minutes
Implementing Governance Automation with Machine Learning4 minutes
Evaluating Governance Effectiveness 5 minutes
Security in Governance Frameworks5 minutes
Advanced Governance Patterns4 minutes
Future Trends in AI & Data Strategy 6 minutes
Tying it all back5 minutes

3 assignmentsTotal 80 minutes

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

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Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructors

David Drummond

Fractal Analytics

3 Courses1,602 learners

Offered by

Fractal Analytics

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Frequently asked questions

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.