Practice Fairness - 16.2.1 | 16. Ethics and Responsible AI | Data Science Advance
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

Fairness

16.2.1 - Fairness

Enroll to start learning

You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is fairness in the context of AI?

💡 Hint: Think about how equality plays a role in technology.

Question 2 Easy

Name one example of where AI bias has occurred.

💡 Hint: Consider areas like justice or hiring.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What principle ensures that AI does not favor one group over another?

Bias Audit
Fairness
Transparency

💡 Hint: Consider which term emphasizes equal treatment in AI.

Question 2

TRUE or FALSE: The COMPAS algorithm is known for its fairness across demographics.

True
False

💡 Hint: Recall the issues associated with this specific algorithm.

Get performance evaluation

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Design a framework to evaluate the fairness of hiring algorithms in a multinational corporation. Consider different demographic factors.

💡 Hint: Think of which aspects of hiring can introduce bias.

Challenge 2 Hard

Critique the implications of historical bias in AI training data. How can this influence societal structures?

💡 Hint: How does data reflect real-world challenges?

Get performance evaluation

Reference links

Supplementary resources to enhance your learning experience.