Course Description

The field of machine learning has experienced significant growth over the last few years, becoming an essential component of numerous industries. From finance to healthcare, machine learning algorithms are used to make predictions, automate decision-making processes, and gain insights from data. However, as these algorithms become more sophisticated, concerns over their fairness and ethical implications have emerged. In this course, we will explore fairness in machine learning in the context of international development. We will discuss how these algorithms are used in developing countries, and the challenges associated with creating fair and equitable models. We will also examine the potential consequences of using biased algorithms in international development, and how they can perpetuate existing inequalities. Throughout the course, we will review different approaches to ensuring fairness in machine learning, including algorithmic auditing, dataset manipulation, and counterfactual analysis. We will also discuss the ethical implications of these approaches, and how they can be applied in real-world settings. The course will begin with an introduction to the basics of machine learning, including supervised and unsupervised learning, regression, and classification. We will then delve into the concept of fairness in machine learning, examining different definitions of fairness and their practical implications. We will also discuss the role of data and how it can be used to detect and mitigate bias in machine learning algorithms. As we progress through the course, we will explore case studies from different sectors, including healthcare, finance, and criminal justice. We will analyze the implications of using biased algorithms in these areas and the potential consequences for marginalized communities. By the end of this course, students will have a deep understanding of fairness in machine learning and the ethical implications of using biased algorithms. They will also have gained practical skills in creating fair and equitable models for international development. This course is ideal for data scientists, policymakers, and development practitioners who want to ensure their work is ethically sound and benefits all members of society.
Author: Dr. Richard Fletcher, Prof. Daniel Frey, Dr. Mike Teodorescu, Amit Gandhi, Audace Nakeshimana (MIT OpenCourseWare)