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Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is bias in the context of machine learning?
π‘ Hint: Think about how data reflects societal stereotypes.
Question 2
Easy
Name one method used to ensure fairness in ML models.
π‘ Hint: Consider how different demographic groups are treated by AI.
Practice 4 more questions and get performance evaluation
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What is the primary purpose of Explainable AI?
π‘ Hint: Think about how users benefit from understanding AI decisions.
Question 2
True or False: Bias is always intentional in machine learning.
π‘ Hint: Consider how societal biases can surface in technology.
Solve and get performance evaluation
Push your limits with challenges.
Question 1
Design a machine learning approach to address bias in credit scoring models. Describe interventions across each stage of the machine learning lifecycle.
π‘ Hint: Think about the sources of bias at different stages of the ML process.
Question 2
Evaluate the ethical implications of using AI in healthcare diagnostics where bias might affect treatment recommendations. Propose a framework for addressing these biases.
π‘ Hint: Focus on equitable access to healthcare as a primary concern.
Challenge and get performance evaluation