Course Details in 2024/25 Session

Module Title	LM AI for Global Challenges
School	Government
Department	International Development
Module Code	08 40110
Module Lead	Slava Jankin
Level	Masters Level
Credits	20
Semester	Semester 2
Pre-requisites
Co-requisites
Restrictions	None
Contact Hours	Lecture-10 hours Seminar-20 hours Guided independent study-170 hours Total: 200 hours
Exclusions
Description	This practical module focuses on hands-on application of AI techniques to global sustainability issues. Students will develop technical skills in core machine learning algorithms, programming, and end-to-end implementation of AI solutions resilient to real-world uncertainties. The module covers research methods including data analysis, evaluation, and synthesis. Working on projects tied to the UN Sustainable Development Goals, students will apply machine learning techniques to address needs in areas like healthcare, education, climate resilience, and democracy. Throughout, students will evaluate ethical implications, inclusion, unintended harms, and the suitability of AI innovations for underserved communities. By designing human-centric solutions that empower marginalised groups, students will demonstrate analytical, creative, and communication skills for conveying nuanced perspectives on deploying AI responsibly. Overall, this module equips students with essential technical abilities, alongside an ethical mindset, for leveraging the potential of AI to help tackle pressing global challenges.
Learning Outcomes	By the end of the module students should be able to: Apply core machine learning techniques and algorithms to build AI/ML models for sustainable development challenges Utilise research skills and methods, including literature reviews, analytical synthesis, data analysis, and report writing Assess ethical, social and environmental implications of AI systems and innovations in sustainable development Design human-centric and inclusive AI solutions that empower marginalised communities Communicate effectively about risks, unintended consequences and appropriateness of AI applications for underserved communities
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.
Other
Reading List