Description
While fairness has become an increasingly popular topic in machine learning and data science, many data scientist struggle with how to incorporate fairness assessments and unfairness techniques into their work. We will cover formal frameworks for assessing ML systems for fairness-related harms and how to apply different algorithmic techniques for mitigating unfairness in trained models.