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.
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 ...Show More
WHAT YOU'LL LEARN
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
An understanding of the role and importance of AI fairness
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TAKEAWAY SKILLS
Content
1.
Introduction to AI Fairness
3 Lessons
Get familiar with AI fairness, its importance, real-life implications, and regulatory guidelines.
2.
Motivating Example
5 Lessons
Get started with evaluating credit scoring models, addressing fairness, bias, and mitigation strategies.
3.
Measuring Fairness
11 Lessons
Work your way through measuring fairness in AI, balancing protected attributes, group and individual fairness, and various fairness metrics.
4.
Mitigation Methods
8 Lessons
Enhance your skills in mitigating AI bias using diverse pre-, in-, and post-processing methods.
5.
Fairness in Natural Language Processing
6 Lessons
Take a closer look at ensuring fairness in NLP through embeddings, bias detection, and debiasing techniques.
6.
Conclusion
1 Lessons
Focus on ethical AI practices, bias mitigation, and advancing fairness in AI applications.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
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