Lead the GenAI revolution by learning AI fairness principles. Future-proof your skills with Python, ensuring fair algorithms for structural and textual data to create unbiased user experiences.

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02 build model

02 eda

02 is fair

02 unawareness

Project: threshold optimization

03 demographic parity

03 equality_of_opportunity.ipynb

03 equalized_odds

03 predictive_parity

04 TO

04 reductions

05 biased

05 debiased

05 weat

05 aug

05 aug-copy

05 weat-copy

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.

Fundamentals of AI Fairness

Follow step-by-step instructions to compute predictive parity.

Predictive Parity

Introduction to AI Fairness

Motivating Example

Measuring Fairness

Project: Fix Bias Using Threshold Optimization

Mitigation Methods

Project: Make a Fair Classifier

Fairness in Natural Language Processing

Project: Explore Fairness in NLP Models

Conclusion

Check Your Knowledge About Fair AI

Predictive Parity

Predictive parity definition