Foundations of Artificial Intelligence: Ethics, Risks & Regulation

About Course

As artificial intelligence becomes a driving force across every sector, understanding the ethical challenges, risks and regulatory frameworks behind the technology is essential for anyone building or deploying AI.

This FREE AI Ethics course – Foundations of Artificial Intelligence: Ethics, Risks & Regulation – provides a clear and practical introduction to the social implications of AI, with a strong focus on trust, fairness, privacy and accountability.

You’ll explore real-world issues such as AI hallucinations, bias, misinformation and the privacy dilemmas at the heart of modern AI systems. Through accessible lessons and case studies, the course demystifies topics like algorithmic fairness, explainability, surveillance and global regulatory trends, including GDPR and the EU AI Act.

By the end, you’ll understand both the risks and responsibilities of working with AI and gain the tools to help ensure your work advances AI for the benefit of all.

Complete the course and quizzes to earn your free NORAI Connect certificate.

Based on Victor A. Lausas’ acclaimed book, “Artificial Intelligence Made Unlocked” (ISBN 978-952-88-0520-5), this course distils the essentials for anyone ready to understand and apply AI in the real world. If you prefer to read the book instead, you can also get the book for free for a limited time:

Subscribe to the North Atlantic newsletter and download your free copy of Artificial Intelligence Made Unlocked: From Logic to Learning for FREE!

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The book is also on shelves at Lulu for 19.99€.

The difference between AI hallucinations, misinformation, and intentional deception
How AI-generated errors can influence real-world decisions and public trust
Practical methods for detecting and managing AI hallucinations
Where bias in AI systems comes from—and strategies to identify and reduce it
The importance of privacy, consent, and data protection in AI development
Key features and global differences in AI regulation, including the EU AI Act
Why explainability and transparency are vital for trustworthy AI
How ethical, human-centred design can help ensure AI serves society responsibly

Course Content

AI Hallucinations, Misinformation & Trust
AI systems can produce convincing but incorrect information - sometimes by accident. This topic introduces how and why AI "hallucinates", the difference between mistakes and misinformation and how trust can be managed in practice.

Understanding AI Hallucinations and Trust
The Implications of AI-Generated Misinformation
Detection and Mitigation of AI Hallucinations

Algorithmic Bias and Fairness in AI
Even the most advanced AI can reflect the biases of its creators or the data it’s trained on. Here, you’ll explore where bias comes from, its real-world effects and practical steps for making AI more fair and just.

Privacy, Surveillance & Data Protection
Modern AI relies on massive data collection, raising new privacy challenges and risks of surveillance. This topic covers data collection, consent, surveillance issues and practical measures to keep information safe.

AI Regulation – The EU AI Act & Global Rules
The rules for AI are changing rapidly. This topic explores the new regulatory landscape, including the EU AI Act and a global look at how different countries govern AI.

Explainability, Transparency & Social Implications
For AI to be trusted, its decisions must be transparent and understandable. This topic unpacks the "black box" problem, the importance of explainability and how AI shapes society.

About Course

What Will You Learn?

Course Content

AI Hallucinations, Misinformation & Trust
AI systems can produce convincing but incorrect information - sometimes by accident. This topic introduces how and why AI "hallucinates", the difference between mistakes and misinformation and how trust can be managed in practice.

Understanding AI Hallucinations and Trust

The Implications of AI-Generated Misinformation

Detection and Mitigation of AI Hallucinations

Algorithmic Bias and Fairness in AI
Even the most advanced AI can reflect the biases of its creators or the data it’s trained on. Here, you’ll explore where bias comes from, its real-world effects and practical steps for making AI more fair and just.

Where Bias Begins: Unpacking Fairness in AI

When AI Gets It Wrong: Real-World Consequences of Bias

Fixing the Flaws: Strategies to Tackle Algorithmic Bias

Privacy, Surveillance & Data Protection
Modern AI relies on massive data collection, raising new privacy challenges and risks of surveillance. This topic covers data collection, consent, surveillance issues and practical measures to keep information safe.

Privacy and Surveillance: Who’s Watching Whom?

AI Surveillance: Safety or Sacrifice?

Protecting Privacy in the Age of AI

AI Regulation – The EU AI Act & Global Rules
The rules for AI are changing rapidly. This topic explores the new regulatory landscape, including the EU AI Act and a global look at how different countries govern AI.

Understanding the EU AI Act: Rules for Responsible AI

AI Laws Around the World: A Glimpse at Global Regulation

Explainability, Transparency & Social Implications
For AI to be trusted, its decisions must be transparent and understandable. This topic unpacks the "black box" problem, the importance of explainability and how AI shapes society.

Opening the Black Box: Why AI Needs to Be Understood

Why Explainability Matters: Making AI Accountable and Trustworthy

Implementing Transparency: Designing AI Systems People Can Understand

Final Assessment
Quiz covers all major themes: Hallucinations, bias, privacy, regulation, transparency and their real-world effects.

AI Ethics & Society: Risks, Trust, and Responsible Development

Prompt Engineering Mastery: From Foundations to Future

Foundations of Artificial Intelligence: Machine Learning

Foundations of Artificial Intelligence: Symbolic and Rule-Based Reasoning

Foundations of Artificial Intelligence: Ethics, Risks & Regulation

About Course

What Will You Learn?

Course Content

AI Hallucinations, Misinformation & Trust AI systems can produce convincing but incorrect information - sometimes by accident. This topic introduces how and why AI "hallucinates", the difference between mistakes and misinformation and how trust can be managed in practice.

Understanding AI Hallucinations and Trust

The Implications of AI-Generated Misinformation

Detection and Mitigation of AI Hallucinations

Algorithmic Bias and Fairness in AI Even the most advanced AI can reflect the biases of its creators or the data it’s trained on. Here, you’ll explore where bias comes from, its real-world effects and practical steps for making AI more fair and just.

Where Bias Begins: Unpacking Fairness in AI

When AI Gets It Wrong: Real-World Consequences of Bias

Fixing the Flaws: Strategies to Tackle Algorithmic Bias

Privacy, Surveillance & Data Protection Modern AI relies on massive data collection, raising new privacy challenges and risks of surveillance. This topic covers data collection, consent, surveillance issues and practical measures to keep information safe.

Privacy and Surveillance: Who’s Watching Whom?

AI Surveillance: Safety or Sacrifice?

Protecting Privacy in the Age of AI

AI Regulation – The EU AI Act & Global Rules The rules for AI are changing rapidly. This topic explores the new regulatory landscape, including the EU AI Act and a global look at how different countries govern AI.

Understanding the EU AI Act: Rules for Responsible AI

AI Laws Around the World: A Glimpse at Global Regulation

Explainability, Transparency & Social Implications For AI to be trusted, its decisions must be transparent and understandable. This topic unpacks the "black box" problem, the importance of explainability and how AI shapes society.

Opening the Black Box: Why AI Needs to Be Understood

Why Explainability Matters: Making AI Accountable and Trustworthy

Implementing Transparency: Designing AI Systems People Can Understand

Final Assessment Quiz covers all major themes: Hallucinations, bias, privacy, regulation, transparency and their real-world effects.

AI Ethics & Society: Risks, Trust, and Responsible Development

Student Ratings & Reviews

Related Courses

Prompt Engineering Mastery: From Foundations to Future

Foundations of Artificial Intelligence: Machine Learning

Foundations of Artificial Intelligence: Symbolic and Rule-Based Reasoning

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AI Hallucinations, Misinformation & Trust
AI systems can produce convincing but incorrect information - sometimes by accident. This topic introduces how and why AI "hallucinates", the difference between mistakes and misinformation and how trust can be managed in practice.

Algorithmic Bias and Fairness in AI
Even the most advanced AI can reflect the biases of its creators or the data it’s trained on. Here, you’ll explore where bias comes from, its real-world effects and practical steps for making AI more fair and just.

Privacy, Surveillance & Data Protection
Modern AI relies on massive data collection, raising new privacy challenges and risks of surveillance. This topic covers data collection, consent, surveillance issues and practical measures to keep information safe.

AI Regulation – The EU AI Act & Global Rules
The rules for AI are changing rapidly. This topic explores the new regulatory landscape, including the EU AI Act and a global look at how different countries govern AI.

Explainability, Transparency & Social Implications
For AI to be trusted, its decisions must be transparent and understandable. This topic unpacks the "black box" problem, the importance of explainability and how AI shapes society.

Final Assessment
Quiz covers all major themes: Hallucinations, bias, privacy, regulation, transparency and their real-world effects.