Explaining Model Decisions and Fixing Them Through Human Feedback
Explore the intricacies of explaining and improving AI model decisions through human feedback in this 58-minute conference talk by Ramprasaath Selvaraju, a Sr. Machine Learning Scientist at Artera. Delve into algorithms that provide explanations for deep network decisions, focusing on building user trust, incorporating domain knowledge, learning grounded representations, and correcting unwanted biases in AI models. Gain insights into visual explanations, interpretability in AI evolution, and applications in image captioning, visual question answering, and medical AI. Examine topics such as Grad-CAM, multi-modal transformer architectures, contrastive self-supervised learning, and techniques for making models resilient to background changes. Learn about innovative approaches like Human Importance-aware Network Tuning (HINT) and Sub-Question Importance-aware Network Tuning for improving AI performance and addressing biases in vision and language models.