MIT 6.S191: Robust and Trustworthy Deep Learning

MIT Introduction to Deep Learning 6.S191: Lecture 5
Robust and Trustworthy Deep Learning
Lecturer: Sadhana Lolla (Themis AI, https://themisai.io)
2023 Edition

For all lectures, slides, and lab materials: http://introtodeeplearning.com​

Lecture Outline
0:00 – Introduction and Themis AI
3:46 – Background
7:29 – Challenges for Robust Deep Learning
8:24 – What is Algorithmic Bias?
14:13 – Class imbalance
16:25 – Latent feature imbalance
20:30 – Debiasing variational autoencoder (DB-VAE)
23:24 – DB-VAE mathematics
27:40 – Uncertainty in deep learning
29:50 – Types of uncertainty in AI
32:48 – Aleatoric vs epistemic uncertainty
33:29 – Estimating aleatoric uncertainty
37:42 – Estimating epistemic uncertainty
44:11 – Evidential deep learning
46:44 – Recap of challenges
47:14 – How Themis AI is transforming risk-awareness of AI
49:30 – Capsa: Open-source risk-aware AI wrapper
51:51 – Unlocking the future of trustworthy AI


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