Synthetic Data as the Future of AI Privacy, Explainability, and Fairness: An Introduction for Data Scientists and Data Executives

Course details

The European Commission’s JRC sees synthetic data as “the key enabler for artificial intelligence,” and analyst firm Gartner predicts that, by 2024, 60% of all AI training data will be synthetic—yet many data and AI professionals are not yet familiar with this privacy-enhancing technology. In this course, synthetic data and privacy expert Alexandra Ebert teaches you the fundamentals of AI-generated synthetic data. Learn how synthetic data can help you stay in compliance with privacy laws. Find out what AI-generated synthetic data is and go over its benefits and limitations. Explore how synthetic data can support your organization’s strategic objectives. Dive into ways to assess synthetic data’s quality and discover best practices for getting started with synthetic data and introducing it within your organization. Plus, learn about different ways synthetic data is used, particularly in the context of Responsible AI or Ethical AI.

Chapters of the course

0. Introduction

   • Why synthetic data is changing AI, data and privacy

1. Why is Synthetic Data Important?

   • The business problem of data vs. privacy
   • The pitfalls of legacy anonymization
   • Chapter Quiz

2. Synthetic Data Fundamentals

   • What is synthetic data?
   • How is synthetic data generated?
   • What are the benefits of synthetic data?
   • What are the limitations of synthetic data?
   • The different categories of synthetic data
   • Chapter Quiz

3. Synthetic Data Use Cases

   • What are the top synthetic data use cases?
   • Which industries benefit the most from synthetic data?
   • Synthetic data for privacy: Preserving AI training
   • Synthetic data for external data sharing
   • Synthetic data for digital product development
   • Synthetic data for open data and data democratization
   • Additional uses: Data augmentation, simulation, and RAI
   • Chapter Quiz

4. The Executive's Guide to Synthetic Data

   • What executives should know about synthetic data
   • How to tie synthetic data to your strategic objectives
   • Best practices for introducing synthetic data
   • Measuring the business impact of synthetic data
   • Building trust in synthetic data
   • Chapter Quiz

5. The Data Scientist's Guide to Synthetic Data

   • How to get started with synthetic data as a data scientist
   • Hands-on: Generating privacy-preserving synthetic data
   • Evaluating synthetic data quality and accuracy
   • Hands-on: Synthetic data for machine learning
   • Understanding why synthetic data is privacy-safe
   • Chapter Quiz

6. Synthetic Data for Responsible AI

   • Quick recap: What is Responsible AI?
   • Why is synthetic data essential for Responsible AI?
   • AI fairness and algorithmic bias and mitigation
   • XAI
   • Hands-on: Synthetic data for explainable AI
   • Synthetic data for RAI assurance and governance
   • Chapter Quiz

7. Conclusion

   • How to continue your synthetic data journey

Notes about the exercise files:

• The notebooks here are made from Google Colaboratory.
• The two notebooks are for the hands-on activies in Chapter 5 (Hands-on: Synthetic data for machine learning) and Chapter 6 (Hands-on: Synthetic data for explainable AI).
• The dataset included here are the needed for both notebooks.
• The PDFs here are handouts included with the course exercise files.
• This repository is mainly for reference.