S3E1: "Privacy-preserving Machine Learning and NLP" with Patricia Thaine (Private AI)

The Shifting Privacy Left Podcast

My guest this week is Patricia Thaine, Co-founder and CEO of Private AI, where she leads a team of experts in developing cutting-edge solutions using AI to identify, reduce, and remove Personally Identifiable Information (PII) in 52 languages across text, audio, images, and documents.
In this episode, we hear from Patricia about: her transition from starting a Ph.D. to co-founding an AI company; how Private AI set out to solve fundamental privacy problems to provide control and understanding of data collection; misunderstandings about how best to leverage AI regarding privacy-preserving machine learning; Private AI’s intention when designing their software, plus newly deployed features; and whether global AI regulations can help with current risks around privacy, rogue AI and copyright.
Topics Covered:

• Patricia’s professional journey from starting a Ph.D. in Acoustic Forensics to co-founding an AI company
• Why Private AI’s mission is to solve privacy problems and create a platform for developers to modularly and flexibly integrate it anywhere you want in your software pipeline, including model ingress & egress
• How companies can avoid mishandling personal information when leveraging AI / machine learning; and Patricia’s advice to companies to avoid mishandling personal information
• Why keeping track of ever-changing data collection and regulations make it hard to find personal information
• Private AI's privacy-enabling architectural approach to finding personal data to prevent it from being used by or stored in an AI model
• The approach that Privacy AI took to design their software
• Private AI's extremely high matching rate, and how they aim for 99%+ accuracy
• Private AI's roadmap & R&D efforts
• Debra & Patricia discuss AI Regulation and Patricia's insights from her article 'Thoughts on AI Regulation'
• A foreshadowing of AI’s copyright risk problem and whether regulations or licenses can help
• ChatGPT’s popularity, copyright, and the need for embedding privacy, security, and safety by design from the beginning (in the MVP)
• How to reach out to Patricia to connect, collaborate, or access a demo
• How thinking about the fundamentals gets you a good way on your way to ensuring privacy & security

Resources Mentioned:

• Read: Yoshua Bengio’s blog post: "How Rogue AI's May Arise"
• Read: Microsoft's Digital Defense Report 2023
• Read Patricia’s article, “Thoughts on AI Regulation”

Guest Info:

• Connect with Patricia on LinkedIn
• Check o

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Hoofdstukken

1. S3E1: "Privacy-preserving Machine Learning and NLP" with Patricia Thaine (Private AI) (00:00:00)

2. Introducing Patricia Thaine, Founder & CEO at Private AI. (00:01:38)

3. Why Patricia chose to co-found Private AI, the company's mission, and some key privacy-enabling features (00:03:35)

4. How companies can avoid mishandling personal information when leveraging AI / machine learning (00:07:26)

5. Why it is so difficult to discover personal information in the first place (00:08:56)

6. Private AI's privacy-enabling architectural approach to finding personal data and preventing it from being used by or stored in an AI model (00:12:10)

7. Private AI's extremely high matching rate, and how they aim for 99%+ accuracy (00:13:51)

8. Private AI's roadmap & R&D efforts (00:15:21)

9. Debra & Patricia discuss AI Regulation and Patricia's insights from her article 'Thoughts on AI Regulation' (00:17:31)

10. The importance of licensing data sets to respect copyright and enfranchise consumers (00:28:31)

11. How listeners can reach out to Patricia, collaborate, or access a demo (00:34:26)

12. How thinking about the fundamentals gets you a good way on your way to ensuring privacy & security (00:35:12)