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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
Define Historical Bias.
💡 Hint: Think about societal influences on the data used.
Question 2
Easy
What does Fair Representation Learning aim to do?
💡 Hint: Focus on fairness and representation.
Practice 4 more questions and get performance evaluation
Interactive Quizzes
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What is 'Bias' in machine learning?
- Systematic prejudice in AI outcomes
- High predictive accuracy
- Data collection method
💡 Hint: Focus on the unfair outcomes caused by AI.
Question 2
True or False: Fair Representation Learning means completely removing sensitive attributes from the data.
- True
- False
💡 Hint: Think about the balance between features.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Critically analyze a machine learning model that was trained on biased historical data. What specific biases could arise, and what strategies would you recommend for addressing them?
💡 Hint: Consider the historical context of your data.
Question 2
Design a plan for creating a biased-free AI system. Outline steps for data management, model training, and implementation, ensuring fairness at each phase.
💡 Hint: Focus on holistic strategies that encompass all ML lifecycle stages.
Challenge and get performance evaluation