04-651   Applications of Artificial Intelligence in Africa

Course discipline

MS EAI

Course description

Recent advances in AI and Machine Learning and an increase in the availability and collection of massive data sources, such as satellite images, social media data, and call detail records from mobile phone operators, have begun to transform our understanding of critical challenges facing the developing world, especially within the African continent. These data sources can offer a more timely, complete, and cost-efficient alternative to survey data that has the potential to guide more effective decision-making. However, in order to maximize the value of alternative data, users need to have an awareness of their sources and value, knowledge of how and when to use such data to obtain meaningful insights, and a deeper appreciation of a range of ethical issues, such as privacy and consent.
In this course, students will become conversant with current research that uses varied types and sources of data to better understand the social and economic realities of people and communities in African countries.

Through readings of recent work, students will also analyze the opportunities and challenges presented by using alternative datasets. Upon successful completion of this course, students will be more familiar with the use of current alternative data sources in research. In addition, students will be able to formulate research questions that are grounded in a review of relevant literature and answerable through an analysis of alternative data types and identify existing data sources and relevant machine learning approaches that can be used to answer these questions.

Learning objectives

This course is designed as a seminar. Therefore, it is based on interactions between the students and the instructor, grounded on readings of current research as well as students’ own writing. Guest speakers involved in the implementation of AI/ML services will be scheduled throughout the semester. It is particularly important that class members take advantage of these sessions to ask in-depth questions as to the design, data, and delivery experiences of these services in order to gain insight into your own individual project possibilities.

Outcomes

By the end of this course, you will be better able to:

• Critically analyze current research that uses AI and machine learning approaches with alternative datasets
• Discuss the opportunities presented by these approaches in understanding and improving outcomes in various sectors of African countries, such as healthcare and financial inclusion
• Analyze the limitations and challenges presented by these datasets, such as privacy and ethical concerns
• Develop an original project proposal grounded in the current literature which addresses an application of AI/ML or data preparation appropriate to development in Africa

Content details

• Week 1: Goals of this course, what is AI, and why is it important for Africa?
• Week 2: Overview of the use of AI and alternative data in Africa
• Week 3: Healthcare and Epidemics: Covid and Malaria
• Week 4: E-commerce
• Week 5: Finance/Marketing
• Week 6: Privacy/Transparency/Ethics
• Week 7: Agriculture and Economic Evaluation
• Week 8: Public Sector: Services and Political Participation
• Week 9: Education
• Week 10: Infrastructure and Urban Planning
• Week 11: Environment
• Week 12: Information and Communication
• Week 13: Corporate Services

Prerequisites

None, although a basic understanding of the AI and machine learning fields would be beneficial.

Faculty

Edith Luhanga