Module overview
This module provides you with an understanding of contested Responsible AI principles such as fairness, transparency, privacy, and inclusiveness. The module explores practical measures for embedding the principles in AI technologies deployed across critical sectors, from education, employment, health, and financial services to criminal justice domains. Given the module’s interdisciplinary and global outlook, it is suitable for postgraduate students from diverse jurisdictions, professional backgrounds, and academic disciplines.
Aims and Objectives
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- Understand the legal, societal, and ethical challenges of AI across Global North and South regions.
- Understand methodological approaches to identifying and mitigating AI Challenges.
- Synthesise interdisciplinary perspectives on Responsible AI.
- Assess the prospects and limitations of the evolving AI governance landscape.
- Understand the legal, societal, and ethical challenges of AI across critical private and public sector domains, focusing on health, education, employment, finance, and criminal justice.
- Understand how to embed Responsible AI principles in AI design and implementation.
Syllabus
Typically:
- Foundations of Responsible AI (history and evolution of the concept)
- Interdisciplinary perspectives on Responsible AI (insights from law, criminology and criminal justice studies, sociology, and STS)
- Responsible AI beyond borders: Perspectives from the Global North and South
- Contested definitions of AI fairness
- AI bias, transparency, explainability, and accountability challenges
- AI-driven privacy violations
- The privatisation of AI design: Implications for digital inclusion and democracy
- Designing Responsible AI for social progress: The relevance of UN sustainability goals
- AI governance frameworks: Prospects and gaps
- Responsible AI futures: Exploring conflicting visions of AI-driven dystopia and utopia
- Future directions in AI governance
Learning and Teaching
Teaching and learning methods
The programme employs a range of teaching and learning methods tailored to online delivery and the needs of working professionals. One of the primary methods used is asynchronous learning, where students can access materials on their own schedule. This includes multimedia resources - but not just video lectures, but also podcasts, animations, and interactive simulations; and reading materials like PDFs or e-books. These resources allow learners to engage with content at their own pace. In addition, discussion forums provide a space for students to ask questions and participate in debates with their peers without the need for everyone to be online at the same time. The asynchronous learning is complemented by synchronous components, such as webinars. These sessions, typically held via Microsoft Teams, give students the opportunity to interact with instructors in real-time, asking questions or participating in discussions. All of these methods are designed to accommodate different learning approaches and ensure that students can apply theoretical knowledge to practical scenarios relevant to their professional contexts. With a strong emphasis on self-paced learning, supported by ongoing instructor guidance.
| Type | Hours |
|---|---|
| Guided independent study | 34 |
| Independent Study | 90 |
| Online Course | 26 |
| Total study time | 150 |
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Presentation | 20% |
| Coursework | 80% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
| Method | Percentage contribution |
|---|---|
| Coursework | 100% |
Repeat Information
Repeat type: Internal & External