Synthetic Data as the Future of AI Privacy, Explainability, and Fairness: An Introduction for Data Scientists and Data Executives
2h 12mAdvanced2023-08-31
Authors
Alexandra Ebert
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.
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.
Skills covered
Data Readiness for AIPrivacyDeploying and Operating AI SystemsAI for Data Engineers and ScientistsData PrivacyRole-Based AI ApplicationsCybersecurityData ScienceOne-Off
Concepts
Introduction
- Why synthetic data is changing AI, data, and privacy
Why Is Synthetic Data Important
- The business problem of data vs. privacy
- The pitfalls of legacy anonymization
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
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
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
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
Synthetic Data for Responsible AI
- Quick recap - What is Responsible AI
- Why is synthetic data essential for Responsible AI
- AI fairness and algorithmic bias mitigation
- XAI
- Hands-on - Synthetic data for explainable AI
- Synthetic data for RAI assurance and governance
Conclusion
- How to continue your synthetic data journey