Dr. Umang Bhatt explores the technical and organizational frameworks required to achieve transparency and explainability in generative AI systems.
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Summary
In this episode, we sit down with Dr. Umang Bhatt, Assistant Professor at NYU and Senior Research Associate at the Alan Turing Institute. Umang is a world-class researcher at the intersection of AI explainability and human-machine interaction.
The conversation focuses on the "how" and "why" of model transparency, exploring practical methods for explaining generative AI outputs to non-technical end-users and the importance of establishing robust feedback loops between internal stakeholders.
You can listen on Spotify or Apple Music or watch the episode here on YouTube.
Key takeaways
Differentiating between interpretability for engineers and explainability for end-users.
Technical approaches to restricting generative AI outputs to ensure safety.
Closing the loop: Why compliance teams must communicate with development leads.
Leveraging AI for personalized education while maintaining high ethical standards.
Global perspectives: Promoting responsible AI frameworks beyond the US, EU, and China.
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組織がAIを採用し、管理し、監視する能力を、企業レベルの信頼性で強化します。規模で運営する規制対象の組織向けに構築されています。