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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 Setup

1. Introduction

2. Preparing to train a model

3. Scientific validity in the modeling process

4. Model evaluation and fairness

5. Explainability versus interpretability

Current Chapter 6. Explainabile AI methods

7. Distribution shift and OOD detection

8. Documenting and releasing a model

Key Points
Glossary
Learner Profiles
Reference

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Explainabile AI methods

Last updated on 2024-04-18 | Edit this page

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