Exploring Fairness in Machine Learning for International Development

In an effort to build the capacity of the students and faculty on the topics of bias and fairness in machine learning (ML) and appropriate use of ML, the {{% resource_link "a252940b-dd5d-4c68-b6a3-22a5802d1933" "MIT CITE" %}} team developed capacity-building activities and material. This material covers content through four modules that an be integrated into existing courses over a one to two week period.

Syllabus

Case Studies with Data: Mitigating Gender Bias on the UCI Adult Dataset
Case Study: Identifying and Mitigating Unintended Demographic Bias in Machine Learning for NLP
Exploring Fairness in Machine Learning: Background
Fairness Criteria, Exploring Fairness in Machine Learning
Introduction to Ethics in Machine Learning
Protected Attributes and "Fairness through Unawareness," Exploring Fairness in Machine Learning
Pulmonary Health Case Study: Bias Exploration, Exploring Fairness in Machine Learning
Solar Lighting Example, Exploring Fairness in Machine Learning
USAID Appropriate Use Framework, Exploring Fairness in Machine Learning