Practice Transparency and Explainability - 16.2.2 | 16. Ethics and Responsible AI | Data Science Advance
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Transparency and Explainability

16.2.2 - Transparency and Explainability

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Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is transparency in the context of AI?

💡 Hint: Think about what it means to understand the workings of a system.

Question 2 Easy

Define explainability in AI terms.

💡 Hint: Consider how you would need to understand your teacher's grading.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does transparency in AI refer to?

Visibility of user data
Understanding the decision-making process
Speed of data processing

💡 Hint: Think about what it means to see inside a system.

Question 2

True or False: Explainability is unnecessary in low-stakes AI applications.

True
False

💡 Hint: Consider the importance of understanding any decision that affects you.

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Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Analyze a scenario where an AI model made a critical decision in the healthcare sector without transparency. Discuss the potential consequences.

💡 Hint: Consider who is affected if a wrong diagnosis is made.

Challenge 2 Hard

Propose a method to enhance explainability in autonomous vehicle decision-making systems.

💡 Hint: Think about how you would explain a car's choices to a passenger.

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