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Trustworthy AI: Explainability, Bias, and Fairness
Trustworthy AI: Explainability, Bias, and Fairness
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Trustworthy AI: Explainability, Bias, and Fairness
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EPISODES
Summary and Schedule
1. Introduction
2. Preparing to train a model
3. Scientific validity in the modeling process
4. Model evaluation and fairness
5. Explainability versus interpretability
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6. Explainabile AI methods
7. Distribution shift and OOD detection
8. Documenting and releasing a model
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Explainabile AI methods
Last updated on 2024-04-18 |
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