Answer Posted / Sukh Beer Singh

AI developers can ensure ethical handling of sensitive data by implementing several practices. Firstly, they should follow data protection regulations such as GDPR and CCPA. Secondly, they should use techniques like differential privacy to anonymize the data. Thirdly, data minimization principles should be followed, collecting only necessary data. Lastly, regular audits should be conducted to ensure compliance.

Explain demographic parity and its importance in AI fairness.

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How do biases in AI models amplify existing inequalities?

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What ethical concerns arise when AI models are treated as "black boxes"?

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What techniques can improve the explainability of AI models?

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What challenges do organizations face in implementing fairness in AI models?

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Can AI systems ever be completely free of bias? Why or why not?

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What is in-processing bias mitigation, and how does it work?

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What are the societal benefits of explainable AI?

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How do societal biases get reflected in AI models?

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What tools or practices can help secure AI models against attacks?

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Explain the difference between data bias and algorithmic bias.

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How do you measure fairness in an AI model?

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How can preprocessing techniques reduce bias in datasets?

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Explain the risks of adversarial attacks on AI models.

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What measures can ensure the robustness of AI systems?

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