Exploring Fairness in Machine Learning for International Development
MIT's CITE team built this four-module unit to teach students and faculty how bias enters machine learning systems and what counts as appropriate use of ML in international development work. Designed to slot into an existing course over one to two weeks, the material walks through case studies and activities on fairness, bias detection, and responsible deployment of algorithms in low-resource and cross-cultural settings. Content is released as open MIT OCW material under a Creative Commons license, so instructors and independent learners can use the slides, exercises, and framing documents directly. This is a compact capacity-building resource rather than a full semester course, aimed at engineers and development practitioners who need a grounded introduction to where ML systems go wrong and how to evaluate them before use in the field.