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What are the societal implications of bias in AI systems?
How do biases in AI models amplify existing inequalities?
How can fairness in AI improve its societal acceptance?
Provide examples of industries where fairness in AI is particularly critical.
How would you address fairness in AI for multi-lingual or global applications?
Why is transparency important in AI development?
What techniques can improve the explainability of AI models?
How does SHAP (Shapley Additive Explanations) contribute to explainability?
Explain the concept of Local Interpretable Model-agnostic Explanations (LIME).
How can explainability improve decision-making in high-stakes AI applications?
What are the challenges of making deep learning models explainable?
How do you balance explainability and model performance?
What ethical concerns arise when AI models are treated as "black boxes"?
How can organizations ensure their AI systems are accountable to users?
What are the societal benefits of explainable AI?
