A Tool for Data Scientists, Decision Makers, and Journalists to Assess Fairness in ML Models

This course aims to teach learners how to assess fairness in machine learning models, specifically focusing on the concept of recourse. By the end of the course, students will be able to audit linear classification models in terms of recourse, enabling individuals to understand and potentially change the decisions made by these models. The course covers topics such as immutable variables, the right to an explanation, ethical considerations for data scientists, optimization approaches, and addressing demographic differences. The teaching method involves a video presentation, and the course is designed for data scientists, decision-makers, and journalists interested in understanding and evaluating the fairness of machine learning models.