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Tutorial Exercises MCQs Interview Q & A Tests
ai-foundation / Ethics and Bias in AI
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Direction: Choose the correct option

Q1.

What is algorithmic bias?
A. A data storage method
B. A programming language
C. A type of computer virus
D. Systematic prejudice in AI outputs
Direction: Choose the correct option

Q2.

Which is a common source of bias in AI systems?
A. Biased training data
B. Too many features
C. Lack of computing power
D. Fast execution
Direction: Choose the correct option

Q3.

What is meant by 'explainability' in AI?
A. Ability to train AI quickly
B. Ability to encrypt AI models
C. Ability to run AI on mobile devices
D. Ability to understand why an AI made a decision
Direction: Choose the correct option

Q4.

Which of the following is an ethical concern with facial recognition technology?
A. All of the above
B. Privacy violation
C. Mass surveillance
D. False positives leading to misidentification
Direction: Choose the correct option

Q5.

What is the 'black box' problem in AI?
A. A security feature
B. A secret algorithm
C. A hardware case
D. The inability to explain how a model reached a conclusion
Direction: Choose the correct option

Q6.

Which principle is fundamental to AI ethics?
A. All of the above
B. Fairness
C. Accountability
D. Transparency
Direction: Choose the correct option

Q7.

The 'right to explanation' in GDPR aims to give individuals:
A. The right to sell data
B. The right to own AI
C. The right to delete AI
D. An explanation of algorithmic decisions affecting them
Direction: Choose the correct option

Q8.

What is 'data poisoning'?
A. Compressing data
B. Backing up data
C. Injecting malicious data to corrupt an AI model
D. Encrypting data
Direction: Choose the correct option

Q9.

Which of the following is a strategy to mitigate bias in AI?
A. All of the above
B. Diverse and representative datasets
C. Regular auditing of models
D. Using fairness metrics
Direction: Choose the correct option

Q10.

An AI system that recommends loans must ensure it does not discriminate based on:
A. Income
B. Employment history
C. Protected attributes (race, gender, etc.)
D. Credit score