Practice Types Of Bias (16.3.1) - Ethics and Responsible AI - Data Science Advance

Practice: Types of Bias

Lesson 16.3.1Ethics and Responsible AI

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9 questions

Flashcard drill
4 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.
Define historical bias in the context of AI.
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Think about how past inequalities can influence current predictions.
Question 2.
What is sampling bias?
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Consider the consequences of only choosing certain demographics.
3Question 3.
What is an example of historical bias?
- A) Data from multiple sources
- B) Salary data showing gender wage gaps
- C) Randomly selected survey participants
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Think about how past data can impact current decision-making.
4Question 4.
True or False: Sampling bias occurs when training data represents the entire target population well.
- True
- False
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Focus on the impact of population representation.
Question 5.
Evaluate how an algorithm might be biased if it's primarily trained on data from one gender. Discuss the potential consequences.
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Consider societal implications and where bias may impact leaders.
Question 6.
Critique an AI system used for law enforcement. How could measurement bias manifest in its algorithms, and what would be the implications?
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Think about how data integrity and accuracy are crucial.

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