Generative AI Demystified: US Congress Data Privacy Strategies

Course Overview:

This course begins by addressing US Congress’ concerns about generative AI and the privacy and data protection issues these models pose. It introduces key players in the generative AI field and discusses four practical applications and their associated concerns. Students will learn the definitions and distinctions between AI and machine learning (ML), as well as an overview of generative AI, including general-purpose AI models, large language models (LLMs), GANs, and GPTs.

The course then covers the technical requirements for training and fine-tuning generative AI, emphasizing the extensive data volumes and parameters involved. It examines the privacy and data protection concerns specific to generative AI models, exploring the link between personal data and these AI systems. Privacy issues related to integrating generative AI with existing technologies are also discussed, along with an introduction to tokens, tokenization, and web scraping.

Legal aspects are examined, including the Italian enforcement measures taken by OpenAI, the legal concerns of crawled datasets, and tools like HaveIBeenTrained that detect non-consensual data scraping. The course further explores how to responsibly handle sensitive data in generative AI applications and the importance of explicit consent.

Students will gain insights into US privacy laws, the need for federal legislation, and snapshots of key privacy laws such as COPPA and state-specific laws. The course also compares privacy legislation frameworks, highlighting three key features, notice and disclosure requirements, opt-out challenges, and data deletion and minimization mandates. Additionally, the relationship between the Federal Trade Commission, privacy, and AI-related enforcement is analyzed.

The course concludes with strategies for privacy-preserving AI, including techniques like homomorphic encryption, zero-knowledge proofs, federated learning, secure enclaves, differential privacy, and synthetic data generation tools.

Course Highlights:

Detailed exploration of US Congress’ views on generative AI and privacy legislation.

In-depth analysis of practical applications and privacy concerns of generative AI.

Examination of technical requirements and data processing in generative AI.

Legal insights into data scraping, privacy laws, and enforcement measures.

Practical strategies for privacy-preserving data sharing and analytics.

Case studies and real-life examples of advanced privacy-preserving techniques like homomorphic encryption and federated learning.