AI Governance: The Fundamentals of AI Governance

Name: AI Governance: The Fundamentals of AI Governance
Rating: 4.5 (5098 reviews)

Create, deploy, and oversee ethical, open, and compliant AI systems for your company.

Bestseller

Role Play

Created byData Universe

Last updated 3/2026

English

What you'll learn

Understand the key principles and structure of AI Governance.
Identify and manage ethical, legal, and operational AI risks.
Apply frameworks such as the EU AI Act and GDPR to real cases.
Define roles, policies, and responsibilities for AI oversight.
Implement controls for explainability and accountability.
Use templates and checklists to ensure traceability and compliance.
Evaluate AI use cases in finance, healthcare, and government.
Develop a responsible, transparent, and compliant AI mindset.

Course content

12 sections • 43 lectures • 2h 13m total length

What can we consider Artificial Intelligence?2:38
What does it mean to govern AI?2:34
Generative AI vs. Classical AI3:07
The role of AI in business and operations2:41
AI in real-world operations and why it differs from other systems2:44
Practical tip: Not all AI needs the same level of governance.2:01

Data sources and licensing for training datasets2:57
Bias in data and mitigation techniques3:41
Consent, data protection, and traceability3:26
Model validation and monitoring through data quality2:58
Assess data quality throughout a model's life cycle to detect data drift, monitor input behavior, and ensure fair, unbiased performance aligned with ethical and business objectives.
Practical tip: Require documented justification for all training datasets.2:45

Security and operational control risks3:19
Ethical, social, and legal risks3:16
Explore the ethical risks of AI, including bias, lack of transparency, privacy concerns, and discrimination, and learn governance tools like ethics committees and impact assessments for responsible AI.
Algorithmic discrimination and lack of explainability2:50
Examine how algorithmic discrimination and lack of explainability intersect with broader social risks in AI governance, such as mass automation, information fragmentation, digital exclusion, inclusion, fairness, and accessibility.
Reputational and regulatory compliance risks3:10
Strategies to detect and manage bias3:20
Practical tip: Define simple ethical criteria and apply them from the start.3:23

Existing regulations impacting AI (e.g., GDPR)3:39
The European AI Act: risk categories and obligations3:43
The European AI Act introduces a risk-based framework classifying AI systems into unacceptable, high and limited risk, with obligations like transparency, documentation, and human oversight.
Regulations in the United States: decentralized approach3:26
Regulation in China: technical requirements and state control3:18
How to stay compliant without limiting innovation3:02
Practical tip: Proper documentation is the first step toward compliance2:39

How to draft internal AI policies, requirements, and guidelines3:35
Draft ai policies, corporate requirements, and guidelines that are clear, practical, and aligned with governance, ethics, data protection; assign owners, evaluate datasets, and require cross‑review from technical, legal, and business.
Version control, records, and justified decisions3:13
Use of templates, minimal records, and operational checklists3:17
Apply agile ai governance with templates, minimal records, and an operational checklist to standardize criteria, assess impact and bias, track unique identifiers, deployment details, and ongoing monitoring.
Practical tip: Use standard templates to reduce dependency on external experts.2:36

Requirements

No technical background required.
Basic knowledge of data or tech governance is helpful but not mandatory.
Ideal for professionals curious about responsible AI adoption.

Description

Every industry is changing due to artificial intelligence, but even the most sophisticated systems can pose significant operational, ethical, and legal risks if they are not properly governed.

The useful and approachable course "AI Governance: The Fundamentals of AI Governance" aims to teach you how to manage AI responsibly, guaranteeing openness, adherence, and confidence throughout the AI lifecycle.

The main elements of AI governance will be covered in the course, including risk management, documentation, organizational roles, ethical principles, and regulatory frameworks like the EU AI Act and GDPR. Real-world examples, templates, and organized checklists will also teach you how to put these ideas into practice.

This course does not require you to be a data scientist or a lawyer. The lessons are straightforward, useful, and aimed at assisting professionals in the public, legal, business, and IT sectors in implementing AI effectively and responsibly.

At the conclusion of the course, you will be able to:

Determine and reduce the risks to ethics and compliance in AI systems.
Create internal accountability frameworks and policies.
Make sure that data and models are transparent and traceable.
Adapt innovation to moral and legal requirements.
This course will equip you with the skills and information you need to create AI that people can trust.

Come along with us and start down the path to a future in which artificial intelligence is not only strong but also accountable, equitable, and explicable.

Who this course is for:

Data, AI, and analytics professionals applying responsible AI practices.
Compliance, legal, and risk officers involved in AI governance.
Business leaders managing AI-driven initiatives and innovation
Consultants and advisors in digital ethics and AI regulation.
IT and data governance teams integrating AI oversight.
Public sector professionals managing algorithmic systems.
Students or researchers exploring AI ethics and compliance.
Anyone seeking to balance innovation and accountability in AI.

AI Governance: The Fundamentals of AI Governance

What you'll learn

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Course content

Introduction to AI Governance6 lectures • 16min

AI Lifecycle: Step-by-Step Governance6 lectures • 18min

Data Governance for AI5 lectures • 16min

Inherent Risks of AI6 lectures • 19min

First half course Practice Test0

Regulation and Compliance6 lectures • 20min

Organizational Structure and Stakeholders5 lectures • 17min

Quick Implementation Guide4 lectures • 13min

Use Cases by Domain4 lectures • 15min

Second half course Practice Test0

Requirements

Description

Who this course is for: