Practice Conceptual Methodologies For Bias Detection (1.2) - 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

Conceptual Methodologies for Bias Detection

Practice - Conceptual Methodologies for Bias Detection

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is historical bias?

💡 Hint: Think about how historical data shapes future predictions.

Question 2 Easy

Name one source of bias in machine learning.

💡 Hint: Consider where bias can enter the ML lifecycle.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does historical bias in ML models indicate?

It reflects existing societal inequalities.
It's purely due to technical errors.
It affects only the input data.

💡 Hint: Think about how historical data influences model training.

Question 2

True or False: Representation bias occurs when training data is too diverse.

True
False

💡 Hint: Think about what happens when data samples are skewed.

Get performance evaluation

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Design an experiment to assess the demographic parity of a loan approval model. What metrics would you choose and why?

💡 Hint: Think about how you can represent different populations in your analysis.

Challenge 2 Hard

Discuss the ethical implications of an AI system that exhibits algorithmic bias. What long-term effects could this have on society?

💡 Hint: Consider both immediate and ripple effects of biased systems.

Get performance evaluation

Reference links

Supplementary resources to enhance your learning experience.