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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does bias in machine learning refer to?
💡 Hint: Think about how outcomes might favor one group over another.
Question 2
Easy
Name one fairness metric.
💡 Hint: Consider measures that assess equality across groups.
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 the definition of bias in machine learning?
- Random errors in the model
- Systematic prejudices leading to unjust outcomes
- Overfitting of the model
💡 Hint: Focus on what bias implies in terms of fairness.
Question 2
True or False: Accountability in AI is only relevant when models are transparent.
- True
- False
💡 Hint: Think about responsibility in various scenarios.
Solve and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
In a lending application, identify three potential sources of bias and propose mitigation strategies for each.
💡 Hint: Consider the entire process from data collection to performance assessment.
Question 2
As a data scientist, how would you address the feedback loop in a predictive policing model that disproportionately targets minority communities?
💡 Hint: Focus on both immediate actions and long-term strategies for fairness.
Challenge and get performance evaluation