Module overview
In this module you will explore the application of Artificial Intelligence (AI) in analysing social problems and formulating public policy responses. You will examine how AI technologies are reshaping our understanding of societal issues and influencing policy-making processes. The module is designed for postgraduate students from various disciplines, particularly those interested in public policy, social sciences, and the societal implications of AI.
Aims and Objectives
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- Design and evaluate AI-augmented strategies for addressing complex social problems.
- Critically evaluate the current and potential applications of AI in social problem analysis and policy formation.
- Assess the legal and policy frameworks governing the use of AI in public administration and policymaking.
- Demonstrate a comprehensive understanding of how AI tools can be used to gather and analyse data for social policy development.
- Analyse the ethical implications and potential biases of AI systems in public policy contexts.
- Articulate the capabilities and limitations of AI applications in policy contexts to various stakeholders.
Syllabus
Typically:
- Introduction to AI technologies relevant to social problem analysis
- AI-driven data collection and analysis methods for social issues
- Case studies of AI applications in public policy (e.g., predictive policing, social service allocation)
- Ethical considerations in AI-assisted policy-making
- Bias detection and mitigation in AI systems for social analysis
- AI and evidence-based policy formation
- Privacy and data protection in AI-driven social research
- The impact of AI on democratic processes and civic engagement
- Challenges and opportunities of AI in addressing global social issues
- Future trends in AI for social problem analysis and policy formation
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 |
|---|---|
| Independent ºÚÁÏÉç | 90 |
| Online Course | 26 |
| Guided independent study | 34 |
| 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