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