Introduction
What it means to think critically about AI
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1. Foundations: AI, Society, and Literacy
Why we should consider AI and society
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Why the ability to think critically is part of AI literacy
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How AI design shapes society
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What AI means to different people
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Why AI systems fail beyond technical capability
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2. Bias, Fairness, and Case Studies
Why bias is not an exception
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How the COMPAS system discriminated against Black defendants
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Why technical fixes are not enough
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Why AI bias is not a mistake
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3. Responsibility, Transparency, and Safety
What responsibility means in AI
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How responsibility, accountability, and liability differ
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Why explainability and transparency matter in AI
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Why AI safety requires robustness
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How AI can be deceived
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4. Information Integrity, Privacy, and Profiling
How misinformation and disinformation spread in the AI era
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Why privacy becomes a critical issue
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How AI-based profiling works
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What resurrection technologies ask us
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How AI changes the nature of work
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Why AI governance is needed
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Why dependency on AI is a growing risk
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