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Learn AI Safety and Ethics

What AI safety actually means, the real risks at individual and societal level, bias, alignment, and how to use AI responsibly.

Understand AI safety beyond science fiction
Identify and account for AI bias in real applications
Apply responsible AI principles to everyday use
Think clearly about AI ethics debates without hype or fear
Free forever No signup required Progress saved locally 15 lessons

Three tracks — pick your level. Progress is saved automatically in your browser.

BeginnerStart here. No prior knowledge assumed.
0 of 5 complete
Lesson 1
What is AI Safety?
AI safety defined — separating science fiction concerns from genuine near-term risks that matter now.
Lesson 2
AI Bias — What It Is and Where It Comes From
How AI inherits biases from training data and how this manifests in real outputs — with concrete examples.
Lesson 3
AI Hallucination and Misinformation
Why AI confidently states false information, the specific failure modes, and the verification habits that matter.
Lesson 4
Privacy and AI
What data AI systems collect, how it is used, and the privacy considerations for personal and professional AI use.
Lesson 5
Disclosure and Transparency
When to disclose AI use — in content, in research, in customer communications — and why transparency is strategic.

What is AI bias?

Why does AI hallucinate?

IntermediateBuild on the basics.
0 of 5 complete
Lesson 1
AI in High-Stakes Decisions
AI in healthcare, hiring, lending, and justice — where AI errors have serious consequences and what safeguards apply.
Lesson 2
Deepfakes and Synthetic Media
AI-generated images, video, audio — the detection approaches and the disclosure norms that responsible use requires.
Lesson 3
Intellectual Property and AI
Training data litigation, copyright in AI outputs, and the current legal landscape for commercial AI use.
Lesson 4
AI and Employment
How AI is changing jobs — which roles are most affected, what skills remain valuable, and how to think about career planning.
Lesson 5
Environmental Impact of AI
Energy consumption, water use, and the carbon footprint of large AI systems — the data and what it means.

What is a deepfake?

What is the AI alignment problem?

AdvancedDepth, edge cases and strategy.
0 of 5 complete
Lesson 1
AI Alignment — What It Really Means
The technical alignment problem — why it is genuinely difficult to specify what we want AI to do.
Lesson 2
Regulatory Landscape
EU AI Act, US executive orders, and the global regulatory response to AI — what it means for users and builders.
Lesson 3
AI Ethics Frameworks
How organisations approach AI ethics — principles, governance structures, and the gap between stated values and practice.
Lesson 4
Responsible AI Use in Organisations
Building responsible AI practices into teams and organisations — policies, training, and governance.
Lesson 5
The Future of AI Safety
Where AI safety research is heading and what the most important open questions are.

What is the EU AI Act?

What does responsible AI use primarily require?

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