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.

Learning objectives
Explain how anonymized data can still be reidentified through various means.
Evaluate why certain scenarios require temporary privacy protection.
Identify the advantages and limitations of using synthetic data compared to traditional datasets.
Explore ways in which synthetic data generation techniques improve data quality.
Assess the ethical implications of using synthetic data in specific applications such as privacy and compliance.
Define explainable AI (XAI) and explain its significance in ensuring transparency and trustworthiness of AI models.

Concepts

0. Introduction

01 - Why synthetic data is changing AI, data, and privacy

1. Why Is Synthetic Data Important

02 - The business problem of data vs. privacy
03 - The pitfalls of legacy anonymization

2. Synthetic Data Fundamentals

04 - What is synthetic data
05 - How is synthetic data generated
06 - What are the benefits of synthetic data
07 - What are the limitations of synthetic data
08 - The different categories of synthetic data

3. Synthetic Data Use Cases

09 - What are the top synthetic data use cases
10 - Which industries benefit the most from synthetic data
11 - Synthetic data for privacy - Preserving AI training
12 - Synthetic data for external data sharing
13 - Synthetic data for digital product development
14 - Synthetic data for open data and data democratization
15 - Additional uses - Data augmentation, simulation, and RAI

4. The Executive's Guide to Synthetic Data

16 - What executives should know about synthetic data
17 - How to tie synthetic data to your strategic objectives
18 - Best practices for introducing synthetic data
19 - Measuring the business impact of synthetic data
20 - Building trust in synthetic data

5. The Data Scientist's Guide to Synthetic Data

21 - How to get started with synthetic data as a data scientist
22 - Hands-on - Generating privacy-preserving synthetic data
23 - Evaluating synthetic data quality and accuracy
24 - Hands-on - Synthetic data for machine learning
25 - Understanding why synthetic data is privacy-safe

6. Synthetic Data for Responsible AI

26 - Quick recap - What is Responsible AI
27 - Why is synthetic data essential for Responsible AI
28 - AI fairness and algorithmic bias mitigation
29 - XAI
30 - Hands-on - Synthetic data for explainable AI
31 - Synthetic data for RAI assurance and governance

Conclusion

32 - How to continue your synthetic data journey

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

Authors

Alexandra Ebert