Key Concepts:
- Data Bias: When the training data doesn’t represent everyone fairly.
- Algorithmic Bias: When a model’s structure favors certain outcomes.
- Human Bias: When developers unintentionally include their assumptions.
1. Understanding AI Ethics
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2. Common Types of Bias in AI
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3. Privacy and Data Protection
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4. Transparency and Explainability
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5. Responsible Use of Generative AI
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6. Ethical AI in Business and Society
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7. Capstone — Build an AI Ethics Framework
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