Explore the critical aspects of robust and trustworthy deep learning in this comprehensive lecture from MIT's Introduction to Deep Learning course. Delve into the challenges of algorithmic bias, including class imbalance and latent feature imbalance, and learn about innovative solutions like the debiasing variational autoencoder (DB-VAE). Examine the importance of uncertainty in deep learning, distinguishing between aleatoric and epistemic uncertainty, and discover methods for estimating both types. Gain insights into evidential deep learning and understand how companies like Themis AI are revolutionizing risk-awareness in AI systems. Conclude with an introduction to Capsa, an open-source risk-aware AI wrapper, and explore the future of trustworthy AI development.