Practice Fair Attribution Principle - 3.3.2.1.1 | Module 7: Advanced ML Topics & Ethical Considerations (Weeks 14) | Machine Learning
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3.3.2.1.1 - Fair Attribution Principle

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does the Fair Attribution Principle aim to achieve?

πŸ’‘ Hint: Consider the concept of fairness in attributing responsibility.

Question 2

Easy

Define Additive Property in the context of SHAP.

πŸ’‘ Hint: Think about how different feature impacts should combine.

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 Fair Attribution Principle primarily concerned with?

  • Determining model accuracy
  • Assigning feature contributions fairly
  • Improving computational efficiency

πŸ’‘ Hint: Think about fairness and contributions.

Question 2

True or False: SHAP provides only global explanations of model behavior.

  • True
  • False

πŸ’‘ Hint: Consider how interpretations can vary.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Analyze a scenario where a model predicts criminal recidivism using features that include socio-economic status and previous offenses. Discuss the implications of fair attribution in this case.

πŸ’‘ Hint: Reflect on historical biases in the data.

Question 2

Consider a healthcare model predicting patient outcomes using various health indicators. How would you apply the Fair Attribution Principle to assess feature contributions?

πŸ’‘ Hint: Think about the importance of transparency in health contexts.

Challenge and get performance evaluation