Practice Sources Of Bias In Ai (16.3) - Ethics and Responsible AI

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11 questions

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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
5 questions · about 3 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is historical bias in AI?
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Think about past societal inequalities.
Question 2.
What does sampling bias mean?
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Consider how sampling can affect results.
3Question 3.
What is historical bias?
- Bias that occurs in real-time data
- Bias derived from systemic inequalities in historical data
- Error from human labeling
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Consider how past practices affect current data.
4Question 4.
Sampling bias occurs when:
- True
- False
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Think about whether a sample can show the whole picture.
Question 5.
Design an AI system to provide equitable loan approvals. Discuss potential biases that could arise in data collection, model training, and outcomes.
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Consider each type of bias and how they could affect loan decisions.
Question 6.
Critically analyze a real-world AI application (like facial recognition). Identify any bias issues and propose measures to mitigate these biases.
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Look at current controversies surrounding the application for insights.

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