Privacy Engineering: Safeguarding AI & ML Systems in a Data-Driven Era; With Guest Katharine Jarmul

Jul 12, 2023

In this episode, renowned data scientist Katharine Jarmul discusses the risks of data privacy and security in ML models. They touch on topics such as OpenAI's ChatGPT, GDPR, challenges faced by organizations, privacy by design, and reputational risk. They emphasize the need for auditability, consent questions, and population selection, as well as promoting a culture of privacy champions. Building models in a secure and private way is crucial, and listeners have a chance to win Katharine's book on practical data privacy.

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Introduction

00:00 • 5min

Privacy Risks in Machine Learning Models

04:39 • 24min

Building Auditability and Privacy by Design into Machine Learning Systems

29:08 • 4min

Bridging the Gap: Privacy Challenges and Solutions

33:02 • 7min

Champion Culture and Reputational Risk in Data Privacy and Machine Learning Era

39:33 • 3min

Privacy, AI, and ML: Building Secure and Private Models

42:43 • 4min

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Welcome to The MLSecOps Podcast, where we dive deep into the world of machine learning security operations. In this episode, we talk with the renowned Katharine Jarmul. Katharine is a Principal Data Scientist at Thoughtworks, and the author of the popular new book, Practical Data Privacy.

Katharine also writes a blog titled, Probably Private, where she writes about data privacy, data security, and the intersection of data science and machine learning.

We cover a lot of ground in this conversation; from the more general data privacy and security risks associated with ML models, to more specific cases such as the case with OpenAI’s ChatGPT. We also touch on things like how GDPR and other regulatory frameworks put a spotlight on the privacy concerns we all have when it comes to the massive amount of data collected by models. Where does the data come from? How is it collected? Who gives consent? What if somebody wants to have their data removed?

We also get into how organizations and professionals such as business leaders, data scientists, and ML practitioners can address these challenges when it comes to risks surrounding data, privacy, security, and reputation. We also explore the practices and processes that need to be implemented in order to integrate “Privacy by Design” into the machine learning lifecycle.

Katharine is a wealth of knowledge and insight into these data privacy issues. As always, thanks for listening to the podcast, for reading the transcript, and supporting the show in any way you can.

With that, we hope you enjoy our conversation with Katharine Jarmul.

Thanks for checking out the MLSecOps Podcast! Get involved with the MLSecOps Community and find more resources at https://community.mlsecops.com.

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