Practice Bias & Fairness (3.4) - The Future of AI – Trends, Challenges, and Opportunities

Practice: Bias & Fairness

Lesson 3.4The Future of AI – Trends, Challenges, and Opportunities

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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.
What is bias in the context of AI?
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Think about how data influences AI decisions.
Question 2.
Define fairness in AI.
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Consider the implications for job hiring.
3Question 3.
What is bias in AI?
- Equal treatment
- Preference for a group
- Algorithmic transparency
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Think about the implications of biased data.
4Question 4.
True or False: Fairness means treating all individuals equally, regardless of their circumstances.
- True
- False
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Consider how equity plays a role.
Question 5.
Consider a financial AI system that minimizes loan approvals for applicants from minority backgrounds. Analyze the sources of bias and suggest comprehensive changes to ensure fairness.
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Reflect on how past data shapes present decisions.
Question 6.
Critically evaluate an insurance company's use of AI to determine premium rates based on zip codes. Discuss the ethical implications and propose a solution.
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Consider how context and personal information can impact outcomes.

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