Detailed Course Outline
Module 1 - AI Principles and Responsible AI
- Google's AI Principles
- Responsible AI practices
- General best practices
Module 2 - Fairness in AI
- Overview in Fairness in AI
- Examples of tools to study fairness of datasets and models
- Lab: Using TensorFlow Data Validation and TensorFlow Model Analysis to Ensure Fairness
Module 3 - Interpretability of AI
- Overview of Interpretability in AI
- Metric selection
- Taxonomy of explainability in ML Models
- Examples of tools to study interpretability
- Lab: Learning Interpretability Tool for Text Summarization
Module 4 - Privacy in ML
- Overview in Privacy in ML
- Data security
- Model security
- Security for Generative AI on Google Cloud
Module 5 - AI Safety
- Overview of AI Safety
- Adversarial testing
- Safety in Gen AI Studio
- Lab: Responsible AI with Gen AI Studio