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Trustworthy AI: Explainability, Bias, Fairness, and Safety
Trustworthy AI: Explainability, Bias, Fairness, and Safety
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Trustworthy AI: Explainability, Bias, Fairness, and Safety
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EPISODES
Summary and Schedule
1. Overview
2. Preparing to train a model
3. Model evaluation and fairness
4. Model fairness: hands-on
5. Interpretablility versus explainability
6. Explainability methods overview
7. Explainability methods: deep dive
8. Explainability methods: linear probe
9. Explainability methods: GradCAM
10. Estimating model uncertainty
11. OOD detection: overview, output-based methods
12. OOD detection: distance-based and contrastive learning
13. OOD detection: training-time regularization
14. Documenting and releasing a model
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Last updated on 2024-06-19 |
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