Answer Posted / Anugya Kumari

Overfitting models to sensitive user data poses several risks, including breaches of privacy and potential discrimination. Overfitting occurs when a model is trained too closely on a specific dataset, leading it to perform poorly on new, unseen data. In the context of sensitive user data, overfitting can result in predictions or decisions that are biased towards the characteristics of the training data, potentially reinforcing existing stereotypes or discrimination.

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

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What measures can ensure the robustness of AI systems?

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How do you measure fairness in an AI model?

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How do societal biases get reflected in AI models?

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Explain demographic parity and its importance in AI fairness.

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What techniques can improve the explainability of AI models?

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What are the societal benefits of explainable AI?

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How do biases in AI models amplify existing inequalities?

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Explain the risks of adversarial attacks on AI models.

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Provide examples of industries where fairness in AI is particularly critical.

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Explain the difference between data bias and algorithmic bias.

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

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

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How can preprocessing techniques reduce bias in datasets?

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

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