Course Purpose
The overall aim of this course is to provide a broad introduction to the principles of responsible AI. The course aims to empower learners with a foundational understanding of responsible AI practices.
Course Learning Outcomes
CLO 1: Explain the fundamental principles of responsible AI.
CLO 2: Demonstrate responsible use of AI tools and technologies through case studies in a given context.
CLO 3: Analyze potential biases or issues that arise in AI applications.
CLO 4: Evaluate responsible approaches when designing, using, or interpreting AI systems.
Course Content
| Module | Description |
|---|---|
| 1. Course Introduction | A foundational overview tracing the evolution from statistics to AI, introducing the need for responsibility as systems grow in influence. |
| 2. Demystifying AI | Explores how AI systems learn and classify, highlighting the importance of human feedback and the risks of misclassification. |
| 3. AI and the Illusion of Autonomy | Reveals the hidden human labour behind AI systems and challenges the myth that these technologies operate independently. |
| 4. Misuse and Misinterpretation | Examines how even accurate AI tools can cause harm if misused or poorly understood, stressing the need for oversight and context. |
| 5. AI and Bias | Practical application of research methodology, including data gathering and testing small cases to find a foothold. |
| 6. More Strategies | Introduces how bias enters AI systems and why its consequences often fall most heavily on marginalised groups. |
| 7. Where does Bias come from? | Explores how bias can enter AI systems through proxy variables, feedback loops, and poor statistical techniques, and how these issues disproportionately affect real people. |
| 8. Core Principles | Explores fairness, accountability, and transparency as core principles of responsible AI, and examines who defines, enforces, and benefits from them across different contexts. |
| 9. Who is Responsible? | This topic explores accountability in AI systems, highlighting who bears the burden when things go wrong - from misinformation to exploitation and environmental harm. |
| 10. Looking to the Future | Not all AI futures are equal: some are inevitable, others speculative. Preparing for what's next means asking the right questions today. |
| 11. Putting it into Practice | This topic ties everything together, helping you apply what you've learned about responsible AI to your own life, work, and community. |