Practice Critical Importance (2.2.2) - Advanced ML Topics & Ethical Considerations (Weeks 14)
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Critical Importance

Practice - Critical Importance - 2.2.2

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is bias in the context of machine learning?

💡 Hint: Think about outcomes that are influenced by the training data.

Question 2 Easy

Define fairness in AI systems.

💡 Hint: Consider how different demographics are impacted by AI decisions.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the primary definition of bias in machine learning?

Fair treatment across demographics
Systematic prejudice in outcomes
Weights assigned to data

💡 Hint: Think about how certain groups might be systematically affected.

Question 2

True or False: Transparency in AI only matters for technical users.

True
False

💡 Hint: Consider the importance of trust in technology.

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Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Design a machine learning project aimed at AI hiring. Identify potential bias points from data collection to model deployment and propose strategies for mitigation.

💡 Hint: Think about the entire lifecycle of the data and the model.

Challenge 2 Hard

Evaluate an AI's fairness in predicting loan approvals. Determine if there’s evidence of disparate impact and suggest improvements.

💡 Hint: Check if the model performs consistently across different demographic groups.

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Reference links

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