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

The Shifting Privacy Left Podcast

Content provided by Debra J. Farber (Shifting Privacy Left). All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Debra J. Farber (Shifting Privacy Left) or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

1+ y ago 36:54

MP3•Episode home

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

Send us a text

Privado.ai
Privacy assurance at the speed of product development. Get instant visibility w/ privacy code scans.
Shifting Privacy Left Media
Where privacy engineers gather, share, & learn
TRU Staffing Partners
Top privacy talent - when you need it, where you need it.
Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.
Copyright © 2022 - 2024 Principled LLC. All rights reserved.

Chapters

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)

63 episodes

#Tech #Entrepreneur #Business #News #Tech News #Privacy #Security #Engineering #Innovation #Ethics #Design #Debra J. Farber (Shifting Privacy Left Media #Development #DevOps #Data Science #Privacy Engineer #Architecture