Practice Core Concept (2.1.1) - Advanced ML Topics & Ethical Considerations (Weeks 14)
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Core Concept

Practice - Core Concept - 2.1.1

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Practice Questions

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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.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

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.

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Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

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.

Challenge 2 Hard

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.

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Reference links

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