Explain the difference between data bias and algorithmic bias.
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How can AI systems be designed to promote inclusivity and diversity?
What is in-processing bias mitigation, and how does it work?
What measures can ensure the robustness of AI systems?
How do you balance explainability and model performance?
How can preprocessing techniques reduce bias in datasets?
How would you handle a conflict between AI performance and ethical constraints?
How do you assess the privacy risks of a new AI project?
Provide examples of industries where fairness in AI is particularly critical.
How do ethical concerns differ between general-purpose AI and domain-specific AI?
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Why is transparency important in AI development?
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