Navigating Data Sensitivity in Language Models

Today, we're joined by Patricia Thaine, co-founder and CEO of Private AI to discuss techniques for ensuring privacy, data minimization, and compliance when using 3rd-party large language models (LLMs) and other AI services. We explore the risks of data leakage from LLMs and embeddings, the complexities of identifying and redacting personal information across various data flows, and the approach Private AI has taken to mitigate these risks. We also dig into the challenges of entity recognition in multimodal systems including OCR files, documents, images, and audio, and the importance of data quality and model accuracy. Additionally, Patricia shares insights on the limitations of data anonymization, the benefits of balancing real-world and synthetic data in model training and development, and the relationship between privacy and bias in AI. Finally, we touch on the evolving landscape of AI regulations like GDPR, CPRA, and the EU AI Act, and the future of privacy in artificial intelligence.

The complete show notes for this episode can be found at https://twimlai.com/go/716.