AI Ethics and Bias: Understanding Fairness in Artificial Intelligence MCQs
Questions: 30
Questions
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1. What is the primary goal of AI ethics?
- a) To make AI systems faster
- b) To ensure the responsible development and use of AI systems
- c) To reduce the cost of AI systems
- d) To replace human decision-making entirely
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2. What does "algorithmic bias" refer to?
- a) Systematic errors in AI systems that lead to unfair outcomes
- b) Improving the accuracy of AI systems
- c) Developing faster training algorithms
- d) Increasing the efficiency of data storage
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3. Which of the following is an example of ethical concerns in AI?
- a) Lack of open-source tools
- b) High computational costs
- c) Discrimination in hiring algorithms
- d) Low hardware compatibility
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4. What is "fairness" in the context of AI?
- a) Ensuring equitable treatment and outcomes for all individuals
- b) Maximizing the efficiency of algorithms
- c) Reducing training time for AI models
- d) Increasing the size of datasets
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5. What is the purpose of "AI explainability"?
- a) To create synthetic data
- b) To optimize the performance of algorithms
- c) To improve hardware compatibility
- d) To make AI decisions transparent and understandable
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6. Which of the following frameworks is widely used to address AI bias?
- a) Fairness through Awareness
- b) Data Encryption Framework
- c) Neural Network Optimization Framework
- d) Blockchain for AI Framework
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7. What does "data bias" refer to in AI systems?
- a) Low computational power of hardware
- b) Errors during model evaluation
- c) Issues in hyperparameter tuning
- d) Skewed or incomplete data that leads to biased model predictions
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8. Which of the following is an ethical issue related to AI surveillance?
- a) Privacy invasion
- b) Increased algorithmic accuracy
- c) Faster processing speeds
- d) Higher hardware compatibility
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9. What is the focus of "inclusive design" in AI development?
- a) Building systems that work equitably across diverse populations
- b) Increasing training speed for AI models
- c) Reducing model size for deployment
- d) Enhancing GPU compatibility
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10. What is "human-in-the-loop" in AI systems?
- a) Building systems without user feedback
- b) Fully automating the decision-making process
- c) Training AI systems without human intervention
- d) Including human oversight in decision-making processes
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11. What is a "black-box model" in AI?
- a) An AI model whose internal workings are not easily interpretable
- b) A simple model with clear transparency
- c) An unsupervised learning algorithm
- d) A model optimized for GPU computation
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12. Which organization provides guidelines on AI ethics?
- a) UNESCO
- b) CERN
- c) NASA
- d) IEEE
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13. What is the significance of "accountability" in AI ethics?
- a) Holding developers and organizations responsible for AI outcomes
- b) Reducing the size of training datasets
- c) Optimizing the learning rate of models
- d) Enhancing the hardware efficiency of systems
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14. What is the role of "bias mitigation techniques" in AI?
- a) Reducing or eliminating biases in AI systems
- b) Increasing the complexity of AI models
- c) Enhancing the processing speed of systems
- d) Decreasing the size of datasets
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15. What does "data anonymization" help achieve in AI systems?
- a) Protecting individual's privacy by removing identifiable information
- b) Increasing the dataset size for better accuracy
- c) Improving the efficiency of data processing
- d) Enhancing hardware utilization during training
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16. What is "AI transparency"?
- a) Reducing the complexity of AI models
- b) Making the processes and decisions of AI systems clear and understandable
- c) Increasing the size of training datasets
- d) Decreasing computation time
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17. What is the ethical concern of "AI weaponization"?
- a) Optimizing neural network architectures
- b) Increasing model complexity
- c) Using AI for harmful or military purposes
- d) Reducing dataset bias
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18. Which principle is essential for responsible AI?
- a) Optimized neural architectures
- b) High computational speed
- c) Reduced hardware costs
- d) Non-discrimination
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19. How can "algorithmic transparency" be ensured?
- a) By documenting and explaining the design and decision processes of AI systems
- b) By using more complex neural networks
- c) By increasing the computational efficiency of models
- d) By reducing the dataset size
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20. Which term describes AI systems that prioritize user safety and well-being?
- a) Efficiency
- b) Beneficence
- c) Scalability
- d) Robustness
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21. What is "AI governance"?
- a) Establishing rules and frameworks for the ethical use of AI
- b) Optimizing algorithms for faster execution
- c) Reducing hardware requirements for AI training
- d) Improving neural network performance
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22. What is "bias amplification" in AI?
- a) When AI models improve their accuracy over time
- b) When AI models reduce dataset sizes
- c) When AI models increase existing biases in the data
- d) When AI models overfit training datasets
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23. What is "ethical AI by design"?
- a) Incorporating ethical principles into AI development from the beginning
- b) Developing AI models without documentation
- c) Increasing the dataset size to reduce biases
- d) Optimizing AI models for faster execution
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24. Which term refers to testing AI systems for fairness across demographic groups?
- a) Backpropagation analysis
- b) Model regularization
- c) Fairness evaluation
- d) Gradient descent optimization
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25. What is "adversarial bias testing"?
- a) Testing AI models by introducing deliberate biases to evaluate their robustness
- b) Reducing dataset size during training
- c) Increasing the complexity of AI models
- d) Enhancing GPU compatibility
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26. What does "proportionality" in AI ethics mean?
- a) Increasing model accuracy at all costs
- b) Ensuring AI systems do not cause harm greater than the benefits they provide
- c) Reducing the computational efficiency of systems
- d) Expanding the dataset size to reduce errors
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27. Which aspect is critical to preventing ethical issues in AI?
- a) Larger training datasets only
- b) High computational power
- c) Advanced neural network architectures
- d) Diverse and representative datasets
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28. What is the significance of "AI accessibility"?
- a) Ensuring AI technologies are available to diverse populations
- b) Increasing the training speed of AI models
- c) Reducing dataset size for faster processing
- d) Optimizing AI algorithms for higher performance
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29. What does the "Right to Explanation" in AI refer to?
- a) Optimizing algorithms for faster execution
- b) The legal requirement for organizations to explain AI decisions
- c) Reducing the computational cost of AI models
- d) Increasing the complexity of neural networks
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30. How can "fairness constraints" be applied in AI systems?
- a) By ensuring equitable treatment for all groups during training and evaluation
- b) By using larger datasets for training
- c) By optimizing the algorithm for better hardware compatibility
- d) By reducing the size of training data
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