Artificial Intelligence Data Fairness and Bias

Overview

In this course, we will explore fundamental issues of fairness and bias in machine learning. As predictive models begin making important decisions, from college admission to loan decisions, it becomes paramount to keep models from making unfair predictions. From human bias to dataset awareness, we will explore many aspects of building more ethical models.

Syllabus

Fairness and protections in machine learning

Welcome to the course! In week one, we will be discussing what fairness means in the context of machine learning and what true parity means in different scenarios

Building fair models: theory and practice

This week we will take action against unfairness. Now that we have an understanding of fairness issues, how do we build models that do not violate them?

Human factors: minimizing bias in data

This week, we will tackle the human biases that enter the data collection and attribute selection processes. The goal? Removing bias before the model is built