Experts discuss the risks and evolving security landscape of AI, emphasizing education in managing AI risks and privacy engineering. They explore legal and ethical implications of AI, balance between utility and privacy, and the importance of safeguarding models and data in AI solutions. The conversation delves into memory, learning, legal frameworks, privacy concerns in large models, and Apple's business strategies in AI.

Hugo chats with Katharine Jarmul, a Principal Data Scientist at Thoughtworks Germany, specializing in privacy and ethics in data workflows. They dive into the vital distinctions between data privacy and security, demystifying common misconceptions. Katharine highlights the impact of GDPR and CCPA, and explores advanced concepts like federated learning and differential privacy. They also tackle real-world issues like privacy attacks and the ethical responsibilities of data scientists, making a compelling case for prioritizing privacy in data practices.

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.