Introduction to Responsible AI in Practice (IRAP) – Outline

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