MSc AI and Sustainability

This module introduces students to the evolving concepts, theories, and practices of global sustainability. It examines sustainable development and different approaches to sustainability transitions, exploring contradictions and challenges posed in practice. Students will learn about and critically analyse dominant and alternative approaches, as well as the assumptions, values, and ethical implications that underpin them. This module aims to provide the critical competencies needed to lead sustainability transitions across different sectors and contexts. 

This core module will be an introduction to concepts in probability theory and statistics and will not have specific adacemic requirements. The purpose of the module is to enable students to take on Statistical Research Techniques in term 2. In addition, it provides the basic knowledge required to study articles engaging in quantitative analysis.

This module introduces students to the principles, methods, and applications of Big Data Analytics and Artificial Intelligence in economic and policy analysis. Students will learn to handle large-scale and unstructured data, apply machine learning algorithms, and interpret model outputs. The module emphasizes ethical use of AI in data-scarce environments from the Global South.

This module will equip students with key data science and AI analytical skills, such as R and Python coding, application programming interfaces, distributed version control and scripting. It is complementary to the econometrics and statistics modules, and will be useful for students seeking to pursue further analytics modules.

This module focuses on sustainability challenges and solutions across inter-related local, national and global contexts. The module critically examines policy and practice across major contemporary sustainability challenges. Taking a multi-actor approach, the module explores the dynamic relationships between grassroots action, governmental policy and private sector , developing critical and applied skills.. The module prepares graduates to become effective change agents capable of navigating the complexities of sustainability careers across organisational contexts and geographical scales.

Evaluation Methods for Economic Policy aims to equip students with a deep understanding of the theoretical concepts underlying policy evaluation
and the practical skills needed to evaluate economic policies. Students will learn various advanced quantitative, qualitative, and mixed methods approaches, examining their strengths and limitations in terms of internal and external validity. The module emphasizes practical applications using open-source computer software such as R and RQDA, based on real-world economic policy issues and case studies from the Global South and Global North.

This course is designed to familiarize students with core policy and reform debates on current global economic governance, with emphasis on the following areas: international monetary and financial integration, international production, international trade and international competitiveness, and the role of labour in the global economy.

This module provides students with a thorough treatment of advanced microeconomic theories and related applied policy debates. This course examines contending approaches to microeconomic theories and their applications focusing on the issues including, but not limited to, consumer behaviour, networks, business cycles, role of the state and micro-foundations of macroeconomic models. Multiple analytical techniques -mathematical, descriptive, graphical, narrative, and historical- will be employed to explain the nature and scope of microeconomic problems and their policy implications.

Students are expected to understand and critically examine theoretical frameworks and arguments so that they can develop analytical skills to deal with real world applied problems.

This module provides an in-depth understanding of mainstream and heterodox approaches to macroeconomic theory, modelling and policy, with a specific focus on sustainability issues. Topics include the macroeconomics of environmental change, shadow banking and financialisation, distribution and growth, green fiscal and monetray policies, financial globalisation, macrofinancial crises and the role of justice in macroeconomics. Students have the opportunity to apply coding techniques to build and run simulations of standard and green macroeconomic models, gaining hands-on experience in computational macroeconomic analysis.

The module introduces students to the topic of green finance and its potential contribution to a sustainable economy in the context of climate change and the transition to a low-carbon economy. Specifically, the module supports students in understanding how climate and other environmental risks create potential financial risks in banking and capital markets and analyses the role of financial actors in driving and potentially mitigating these risks.

This module seeks to provide foundations of economic theory across micro and macro for students without any prior disciplinary knowledge. The course will introduce the most relevant economic theories across micro and macro to enable students to navigate broader economic policy debates that often draw on a set of analytical propositions across micro and macro. 

This module examines advanced machine learning (ML) methods and their integration into modern economic analysis. It covers both predictive and causal approaches, including regularization and model selection (LASSO, Ridge, Elastic Net), ensemble learning (random forests, gradient boosting), and deep learning architectures (neural networks, convolutional and recurrent models). 

This course provides an introduction to the principles and tools of data management and visual analytics. Students learn how to acquire, clean, structure, and store data efficiently, and how to extract insights through effective visual and exploratory analysis. Emphasis is placed on reproducibility, transparency, and ethical practices in handling data of varying scales. The course combines conceptual foundations with empirical applications using Python and R.

This module focuses on a range of ethical issues posed by artificial intelligence (AI), with a particular focus on how AI systems — especially generative AI engines — are shaped by and perpetuate intersecting legacies of coloniality, racial capitalism, sexism and global power asymmetries. Building on a decolonial analytical framework, students will explore how AI technologies embed colonial-imperial practices through algorithmic governance, extractive data economies, labour exploitation and knowledge-production hierarchies. To complement this decolonial approach, the module draws on African, Asian, Middle Eastern, and Euro-American moral theoretic perspectives to conceptually interrogate the moral, cultural, and social implications of AI. Students will engage with case studies across the arts, humanities, social sciences, to examine how AI influences matters such as personhood, memory, communication, translation, cultural preservation, and labour. Students will also analyse the tensions between techno-solutionism and social justice, directly confronting how ever-growing AI technologies may either reinforce or challenge contemporary social inequities.