Episode
The Complex World of Generative AI Governance
- Podcast
- AI Engineering Podcast
- Published
- Dec 1, 2024
- Duration seconds
- 3259
- Processing state
failed- Canonical source
- https://www.aiengineeringpodcast.com/ai-governance-challenges-and-techniques-episode-43
Actions
POST https://stenobird.com/v1/public/podcasts/ai-engineering-podcast/episodes/the-complex-world-of-generative-ai-governance/transcription-requests
Idempotently request low-priority transcript generation for this episode.GET https://stenobird.com/podcast/ai-engineering-podcast/the-complex-world-of-generative-ai-governance.md
Read the agent-friendly Markdown representation of this episode resource.
Summary
Summary In this episode of the AI Engineering Podcast Jim Olsen, CTO of ModelOp, talks about the governance of generative AI models and applications. Jim shares his extensive experience in software engineering and machine learning, highlighting the importance of governance in high-risk applications like healthcare. He explains that governance is more about the use cases of AI models rather than the models themselves, emphasizing the need for proper inventory and monitoring to ensure compliance and mitigate risks. The conversation covers challenges organizations face in implementing AI governance policies, the importance of technical controls for data governance, and the need for ongoing monitoring and baselines to detect issues like PII disclosure and model drift. Jim also discusses the balance between innovation and regulation, particularly with evolving regulations like those in the EU, and provides valuable perspectives on the current state of AI governance and the need for robust model lifecycle management. Announcements Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems Your host is Tobias Macey and today I'm interviewing Jim Olsen about governance of your generative AI models and applications Interview Introduction How did you get involved in machine learning? Can you describe what governance means in the context of generative AI models? (e.g. governing the models, their applications, their outputs, etc.) Governance is typically a hybrid endeavor of technical and organizational policy creation and enforcement. From the organizational perspective, what are some of the difficulties that teams are facing in understanding what those policies need to encompass? How much familiarity with…