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