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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 Setup

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

Current Chapter 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

Key Points
Glossary
Learner Profiles
Reference

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Estimating model uncertainty

Last updated on 2024-06-19 | Edit this page

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