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IB Digital Society: Content: Artificial intelligence

Machine learning, uses, risks and regulation. 2 subtopics, 9 key terms.

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In this topic: 7.1 · 7.2

7.1Machine learning and how AI works

Artificial intelligence: Systems performing tasks that normally need human intelligence.
Machine learning: Systems that learn patterns from data rather than following explicit rules.
Training data: Data used to teach a model.
Neural network: Layers of connected nodes loosely inspired by the brain.
Generative AI: AI that creates text, images, audio or video (e.g. large language models).

Types: supervised (labelled data), unsupervised (finding patterns), reinforcement learning (rewards).

The full notes, worked example and check questions for this subtopic are in the app.

Exam tip: Explain AI in plain steps: data → training → model → prediction → feedback.

Facial recognition: AI identifying people from images.
Automation bias: Over-trusting automated decisions.
EU AI Act: The first comprehensive AI law (adopted 2024), using risk levels.
Explainability: The ability to explain how an AI reached a decision.

Uses: medical imaging, translation, education, policing, hiring, creative work.

The full notes, worked example and check questions for this subtopic are in the app.

Exam tip: For any AI question, consider at least three stakeholders: developers, users, those affected.