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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
Define bias in the context of machine learning.
💡 Hint: Think about how historical data can influence model behavior.
Question 2
Easy
Name one type of bias and give a brief example.
💡 Hint: Consider how training data reflects real-world demographics.
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 term describes systematic prejudice in AI outcomes?
- Equity
- Bias
- Fairness
💡 Hint: Think about how real-world biases reflect in data.
Question 2
Is transparency necessary for trust in AI systems?
- True
- False
💡 Hint: Recall why users need to feel confident in AI applications.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Design a machine learning model for loan approvals. Identify potential sources of bias and propose strategies to mitigate them. Who would you hold accountable for bias in the outcomes?
💡 Hint: Consider who creates the model and uses the data.
Question 2
A company using AI for hiring faces backlash due to discrimination claims. What steps should the company take to address accountability and transparency issues?
💡 Hint: Think about both technical solutions and human oversight.
Challenge and get performance evaluation