You can download the lectures here. We will try to upload lectures prior to their corresponding classes.

  • Lecture 1 - Introduction to Responsible AI
    tl;dr: Overview of AI history, example models (NLP/LLM, Computer Vision, Robotics), and core challenges in responsible AI: explainability, uncertainty, and potential harms
    [slides]

    Topics Covered:

    • What is AI: Agents, perceptions, actions, and environment
    • AI Timeline: From 1900s to 2025+ (AI winters, transformers, LLMs, AGI)
    • Example AI Models:
      • Natural Language Processing (LLM): Transformer, code generation
      • Computer Vision: CLIP, image captioning, medical segmentation
      • Robotics: Vision-Language-Action models (RT-2)
    • AI Responsibility Challenges:
      • Explainability: Understanding model decisions (from linear models to LLMs), Chain-of-Thought limitations, mechanistic interpretability
      • Uncertainty: Aleatoric vs. epistemic uncertainty, impact on LLM performance, robotics navigation, self-driving vehicles
      • AI Harms: Safety (hallucination, jailbreaking, prompt injection, data poisoning), privacy (data leakage, memorization), fairness
  • Lecture 2 - Transformer and Vision Models
    tl;dr: Deep dive into Transformer architecture and its applications in computer vision, including ViT, CLIP, SAM, and multi-modal models
    [slides]

    Topics Covered:

    • Transformer Architecture: Positional embedding, Multi-head attention, Feed-forward layers, Residual connections, Layer normalization
    • Vision Transformer (ViT): Applying transformers to images, patch embedding, scaling laws
    • ViT Variants: MAE (self-supervised pre-training), Swin Transformer (multi-scale patches), DeiT (distillation)
    • CLIP: Contrastive language-image pre-training, zero-shot classification
    • SAM: Segment Anything Model, promptable segmentation
    • LLaVA: Visual instruction tuning, multi-modal reasoning
    • Responsibility Issues: Uncertainty estimation, out-of-distribution detection, fairness, hallucination
  • Lecture 3 - Model-Agnostic Explanations and SHAP
    tl;dr: Post-hoc explanation methods that work on any AI model by approximating complex logic with simpler, local models or using game theory to calculate feature importance
    [slides]

    Topics Covered:

    • Local vs. Global Explanations
    • LIME (Local Interpretable Model-agnostic Explanations)
    • Shapley Values & SHAP
    • Properties of SHAP
  • Lecture 4 - Uncertainty in AI
    tl;dr: How to understand why LLMs produce certain outputs—covering token-level attributions, perturbation and counterfactual explanations, reasoning structures (CoT, ToT), and mechanistic interpretability inside transformers.
    [slides]

    Topics Covered:

    • Gradient-based explanations
    • Perturbation & counterfactual explanations
    • Chain-of-Thought, Tree / Graph of Thoughts, and adaptive reasoning
  • Lecture 5 - AI Safety and Robustness
    tl;dr: The vulnerability of AI systems to malicious attacks and environmental noise.
    [slides]

    Topics Covered:

    • Adversarial Examples
    • Attack Methods
    • Defensive Strategies
  • Lecture 6 - Backdoor Attacks in AI
    tl;dr: Backdoor Attacks, a form of data poisoning where a model is trained to behave normally on clean data but perform specific malicious actions when a hidden trigger is present.
    [slides]

    Topics Covered:

    • Backdoor Attack Mechanism
    • Attack vs. Adversarial Examples
    • Poisoning Strategies
  • Lecture 7 - Responsible Retrieval-Augmented Generation
    tl;dr: This lecture introduces the RAG framework and examines methods to ensure its reliability, specifically focusing on resolving knowledge conflicts and defending against adversarial poisoning of retrieval sources.
    [slides]

    Topics Covered:

    • The Basic RAG Pipeline: Overview of the Indexing, Retrieval, and Generation stages.
    • Improving Factuality: Advanced strategies such as Self-RAG and FLARE which use self-reflection and uncertainty-based retrieval to minimize hallucinations. Knowledge Conflict Resolution: Frameworks like Micro-Act and ASTUTE RAG that allow LLMs to reason through contradictions between their internal memory and retrieved documents.
    • Adversarial Robustness: Analyzing vulnerabilities to corpus poisoning (e.g., PoisonedRAG) and backdoor attacks (e.g., AGENTPOISON) that inject malicious text into the knowledge base to manipulate model outputs.
  • Lecture 8 - Uncertainty in Machine Learning
    tl;dr: This lecture covers the fundamental role of uncertainty in AI, moving beyond raw predictions to provide stakeholders with confidence measures essential for high-risk domains like healthcare and autonomous driving.
    [slides]

    Topics Covered:

    • High-Stakes Use Cases: Why binary predictions are insufficient in Fintech (stock forecasting), Healthcare (tumor diagnosis), and Auto-driving (path planning).
    • Uncertainty vs. Confidence: Definitions and the mathematical intuition that Uncertainty $\approx$ 1 - Confidence.
    • Informed Decision-Making: How uncertainty acts as a complementary layer of information to help humans prioritize risk avoidance over simple model outputs.
  • Lecture 9 - AI Governance and Intellectual Property
    tl;dr: This lecture examines methods for quantifying and managing uncertainty in Vision-Language Models (VLMs), focusing on techniques to detect overconfidence and hallucinations in multimodal tasks.
    [slides]

    Topics Covered:

    • Categorization of Multimodal Uncertainty (Intramodal vs. Intermodal)
    • Core Quantification Methods (Probabilistic Embeddings and Verbalized Uncertainty)
    • Calibration Techniques and Conformal Prediction Benchmarking
  • Lecture 10 - Reinforcement Learning and Safety
    tl;dr: This lecture introduces Distributional Reinforcement Learning (DistRL), focusing on modeling the full probability distribution of returns to enable risk-sensitive decision-making in autonomous systems.
    [slides] [distributed RL]

    Topics Covered:

    • From Expected to Distributional Reinforcement Learning
    • Return Distribution Representations and Bellman Operators
    • Risk-Sensitive Control and Robotics Applications
  • Lecture 11 - Responsible Agentic System
    tl;dr: This lecture investigates the technical, ethical, and management-level risks associated with agentic systems and presents frameworks for building responsible, secure, and auditable AI agents
    [slides]

    Topics Covered:

    • Risks and Challenges in Agentic Systems
    • Security Attacks (Memory Extraction and Prompt Injection)
    • Mitigation Strategies and Auditing Mechanisms
  • Lecture 12 - Agentic Systems
    tl;dr: This lecture explores the transition from static Large Language Models (LLMs) to autonomous agents that can reason, use tools, and interact with the world.
    [slides]

    Topics Covered:

    • From LLM to Agent
    • Agentic Architectures
    • Multi-Agent Systems