This module equips students with practical skills to apply core machine learning techniques and algorithms to tackle policy challenges and improve government operations. Building on technical foundations from the first semester, students will gain hands-on experience developing, evaluating, and communicating the impacts of AI/ML systems in public sector contexts. The module covers research methods including literature reviews, data analysis, and report writing. Students will work with real-world datasets and programming tools to build models addressing issues across areas like public health, education, transportation, criminal justice, and social welfare. Techniques covered include supervised learning, unsupervised learning, computer vision, natural language processing, and tools for interpreting and auditing algorithms. Students will apply analytical and creative thinking to develop end-to-end solutions, from prototyping to productionisation. By the end of the module, students will demonstrate applied skills to responsibly design, develop, and deploy AI to improve government operations, public service delivery, and policymaking. They will also incorporate ethical considerations throughout technical AI projects in political environments.
Learning Outcomes
By the end of the module students should be able to:
Apply core machine learning techniques and algorithms to address policy and governance challenges
Utilise research skills and methods, including literature reviews, data analysis, and report writing
Assess legal, ethical and social implications of deploying AI systems in public sector contexts
Design human-centric AI solutions aligned with public values and subject to oversight
Communicate effectively about the impacts, trade-offs and ethical issues around automating policy and governance with AI
Assessment
Assessment Methods & Exceptions
Assessment:
Independent project (75%): 1000-word written report. Group project which also involves the group presenting the report 10 mins (25%): a 2500-word written report
Reassessment:
Students will be reassessed by failed component.
The re-assessment method for independent project (75%) is a 1000-word written report.
The re-assessment method if a student fails the group component (25%) will be a 700-word written report reflecting on the group project and accompanying written presentation slides communicating the results of the research.
