Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information

This course focuses on teaching learners how to evaluate the fairness of machine learning models when faced with uncertain and incomplete information. The course aims to help students understand the complexities of assessing fairness in such scenarios. The skills taught include techniques for evaluating fairness, especially in the context of uncertain and incomplete data. The teaching method involves theoretical explanations and practical examples to illustrate the concepts. This course is intended for individuals interested in machine learning, fairness, and ethics in AI, particularly those looking to enhance their understanding of evaluating model fairness under challenging conditions.