How Can You Trust Machine Learning?

Explore a comprehensive framework for building trust in machine learning and AI systems in this Stanford University seminar. Delve into Professor Carlos Ernesto Guestrin's discussion on the three pillars of clarity, competence, and alignment that can lead to more effective and trustworthy AI. Examine real-world examples, including visual question answering, type 1 diabetes management, and image classification, to understand the challenges and solutions in creating interpretable and reliable ML models. Learn about techniques such as explanations for neural network predictions, data augmentation, and adaptive loss alignment. Discover how to address the increasing complexity of foundation models and optimize for multiple metrics to enhance the trustworthiness of AI systems in various applications.