HKUST-GZ
Responsible AI
Fall 2025

Responsible AI / Fall 2025

Updates

  • Final Exam
  • Final project presentations
  • New Lecture is up: Lecture 12 - LLM-based Agentic Systems [slides]
  • New Lecture is up: Lecture 11 - LLM-based Agentic Systems [slides]
  • Final project proposal due
  • New Lecture is up: Lecture 10 - Reinforcement Learning and Safety [slides] [distributed RL]
  • Midterm Exam

Course Description

This course explores the ethical, social, and technical challenges in developing and deploying artificial intelligence systems. Students will learn about fairness, accountability, transparency, privacy, and the broader societal impacts of AI technologies. Through lectures, case studies, and hands-on projects, we will examine how to build AI systems that are beneficial, trustworthy, and aligned with human values.

Course Overview

Welcome to Responsible AI! This course examines the ethical, social, and technical dimensions of artificial intelligence systems. As AI becomes increasingly integrated into society, it is crucial for developers, researchers, and practitioners to understand how to build systems that are fair, transparent, accountable, and aligned with human values.

What You Will Learn

  • AI Ethics & Philosophy: Explore fundamental ethical frameworks and their application to AI systems
  • Fairness & Bias: Understand algorithmic bias, fairness metrics, and techniques for building equitable AI
  • Transparency & Explainability: Learn methods for making AI systems interpretable and explainable
  • Privacy & Security: Study privacy-preserving techniques and security considerations in AI
  • Accountability & Governance: Examine policy frameworks, regulations, and best practices for AI governance
  • Societal Impact: Analyze the broader social, economic, and political implications of AI technologies

Course Format

  • Lectures: Weekly lectures covering theoretical foundations and real-world case studies
  • Discussions: Interactive sessions analyzing current AI ethics issues and controversies
  • Assignments: Hands-on projects applying responsible AI principles to practical problems
  • Guest Speakers: Industry experts and researchers sharing insights from the field

Prerequisites

  • Basic understanding of machine learning and AI concepts
  • Programming experience (Python preferred)
  • Interest in ethical and societal issues

For course announcements and updates, please check this website regularly or join our communication channels.

Previous Offerings


Instructors

Sihong Xie

Teaching Assistants

Rufeng Chen

Rui Xu

Jiujiu Chen

Yazheng Liu