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AI Ethics Safety Interview Questions
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What is bias in AI systems? Provide some examples.

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How do societal biases get reflected in AI models?

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Explain the difference between data bias and algorithmic bias.

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What techniques can be used to detect bias in AI systems?

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How can datasets be made more representative to mitigate bias?

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

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Explain demographic parity and its importance in AI fairness.

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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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How does regular auditing of AI systems help reduce bias?

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

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Explain the risks of adversarial attacks on AI models.

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How can unintended consequences in AI behavior be avoided?

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What measures can ensure the robustness of AI systems?

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What is meant by verification and validation in the context of AI safety?

 1

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Un-Answered Questions { AI Ethics Safety }

What role do regulatory bodies play in ensuring AI safety?

1

What is the role of international standards in AI governance?

1

How can post-processing techniques help ensure fairness in AI outputs?

1

Explain the concept of Local Interpretable Model-agnostic Explanations (LIME).

1

Can bias ever be fully removed from AI systems? Why or why not?

1

How can AI developers ensure ethical handling of sensitive data?

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What are the societal benefits of explainable AI?

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What are the risks of overfitting models to sensitive user data?

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What measures should be taken to prevent data misuse in AI?

1

Explain demographic parity and its importance in AI fairness.

1

How would you ensure accountability in AI systems?

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What is the trade-off between personalization and privacy in AI applications?

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

1

How would you address fairness in AI for multi-lingual or global applications?

1

What is differential privacy, and how does it work?

1

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