In this course, you will learn the basic concepts of AI fairness as part of the broader concept of Responsible AI. As AI’s role in our daily lives increases, ensuring algorithms are fair for everyone is becoming increasingly important.

You will use Python with various types of models, starting from simple regression up to transformers. You will learn what AI fairness is, how to measure if a model is fair, and, most importantly, how to fix biased systems. You’ll cover both structural (tabular) and textual data.

After completing this course, you will be able to identify areas where fairness is a concern and plan a strategy for ensuring an unbiased user experience. This strategy will start with identifying bias sources by measuring the problem is seriousness and then implementing remedies. This will help you create better models, especially in areas where your actions significantly impact human lives.