Artificial Intelligence Data Fairness and Bias

LearnQuest via Coursera

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Overview

Artificial Intelligence Data Fairness and Bias is a critical course designed for AI practitioners, data scientists, policymakers, and anyone involved in developing or deploying AI systems. This course explores the complex issues of fairness, bias, and ethics in AI, focusing on how data can influence model outcomes and impact society. You’ll learn to identify different types of biases in datasets, including sampling bias, measurement bias, and historical bias, and understand their effects on AI performance and decision-making. The course also covers...

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