Practice Ethics In Advanced Data Science (1.5) - Introduction to Advanced Data Science

Practice: Ethics in Advanced Data Science

Lesson 1.5Introduction to Advanced Data Science

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9 questions

Flashcard drill
5 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is data privacy?
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Think about how we keep our passwords safe.
Question 2.
What is meant by fairness in machine learning?
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Consider why some models may favor certain groups.
3Question 3.
What is the primary concern of data privacy?
- A) Protecting data from loss
- B) Protecting sensitive user data
- C) Enhancing data quality
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Consider what personal information needs protection.
4Question 4.
True or False: Transparency in data science makes automated decisions less trustworthy.
- True
- False
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Think about how much you trust recommendations from clear explanations.
Question 5.
Assess the ethical considerations in deploying an image recognition model that was trained predominantly on images of light-skinned individuals.
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Consider how the diversity in training data impacts outcomes.
Question 6.
Develop a plan for how a data science team can improve model transparency when presenting their work to stakeholders.
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Think about the steps needed to make complex models understandable.

5 more questions in this section. Enroll free to practise them all.