Responsible AI in Practice: Fairness, Bias & Explainability
About this course
This course offers a solid foundation in Responsible AI, honing in on fairness, bias, and explainability in artificial intelligence systems. You'll explore essential concepts like fairness metrics and bias mitigation, along with techniques such as LIME and SHAP to enhance model interpretability. By the end, you'll be equipped to assess privacy risks and weigh the trade-offs between fairness, privacy, and accuracy in AI design, making it especially relevant for AI professionals and technology practitioners.
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What you'll learn
- explain fairness, interpretability, and privacy concepts in AI
- analyze AI models using explainability and fairness techniques
- apply bias mitigation and privacy-preserving methods
- evaluate trade-offs in responsible AI system design
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