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Fundamentals of AI Fairness
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.
5.0
34 Lessons
3 Projects
4h
Updated 1 month ago
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- An understanding of the role and importance of AI fairness
- Hands-on experience measuring fairness
- Working knowledge of debiasing various types of models
- The ability to build fair models for structured and textual data
Learning Roadmap
1.
Introduction to AI Fairness
Introduction to AI Fairness
Get familiar with AI fairness, its importance, real-life implications, and regulatory guidelines.
2.
Motivating Example
Motivating Example
Get started with evaluating credit scoring models, addressing fairness, bias, and mitigation strategies.
3.
Measuring Fairness
Measuring Fairness
11 Lessons
11 Lessons
Work your way through measuring fairness in AI, balancing protected attributes, group and individual fairness, and various fairness metrics.
4.
Mitigation Methods
Mitigation Methods
8 Lessons
8 Lessons
Enhance your skills in mitigating AI bias using diverse pre-, in-, and post-processing methods.
5.
Fairness in Natural Language Processing
Fairness in Natural Language Processing
6 Lessons
6 Lessons
Take a closer look at ensuring fairness in NLP through embeddings, bias detection, and debiasing techniques.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
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.
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