Practice Regularization With Fairness Constraints (1.3.2.1) - Advanced ML Topics & Ethical Considerations (Weeks 14)
Students

Academic Programs

AI-powered learning for grades 8-12, aligned with major curricula

Professional

Professional Courses

Industry-relevant training in Business, Technology, and Design

Games

Interactive Games

Fun games to boost memory, math, typing, and English skills

Regularization with Fairness Constraints

Practice - Regularization with Fairness Constraints

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is regularization?

💡 Hint: Think about how we keep our models from fitting noise.

Question 2 Easy

Why are fairness constraints important?

💡 Hint: Consider what happens if models reinforce societal biases.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the primary goal of regularization?

To maximize accuracy
To prevent overfitting
To minimize bias

💡 Hint: Think about keeping the model simple.

Question 2

True or False: Fairness constraints are only necessary for models that make critical societal decisions.

True
False

💡 Hint: Consider the implications of bias in all applications.

Get performance evaluation

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

How would you implement regularization with fairness constraints in an AI system predicting loan approvals?

💡 Hint: Focus on how to balance model predictions fairly.

Challenge 2 Hard

Critically discuss the implications of ignoring fairness when deploying a machine learning model in healthcare.

💡 Hint: Consider the ethical responsibilities healthcare providers hold.

Get performance evaluation

Reference links

Supplementary resources to enhance your learning experience.