How can datasets be made more representative to mitigate bias?

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How would you handle bias when it is deeply embedded in the training data?

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How does automation in AI affect job markets and employment?

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How does federated learning enhance data privacy?

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What are the penalties for non-compliance with AI regulations?

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How do you balance explainability and model performance?

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What is the significance of fairness in AI, and how do you define it?

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How would you ensure accountability in AI systems?

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How can anomaly detection systems improve AI safety?

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How does SHAP (Shapley Additive Explanations) contribute to explainability?

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What do you understand by AI safety, and why is it critical?

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What role do ethics boards play in AI governance?

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How do you measure fairness in an AI model?

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What ethical considerations arise in AI systems that learn from user behavior?

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What frameworks or guidelines have you used to address ethical issues in AI projects?

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