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Explore the critical question of trust in machine learning and AI systems in this 52-minute Stanford seminar presented by Carlos Guestrin. Delve into a framework built on three pillars: Clarity, Competence, and Alignment. Discover algorithmic and human processes that can lead to more effective, impactful, and trustworthy AI. Learn about methods for making machine learning predictions more explainable, techniques for rigorously evaluating and testing ML models, and approaches to aligning AI behaviors with desired values. Gain insights into fundamental concepts and actionable algorithms that can increase trust in machine learning. Enhance your understanding of the complexities surrounding AI development and deployment in various aspects of our lives.
