Answer / Ganesh Pratap Singh

Balancing accuracy and fairness in AI systems is challenging because these two objectives often conflict. For instance, increasing fairness may lead to a decrease in accuracy, as a model may be forced to make less accurate predictions for certain groups to ensure equitable outcomes. This tension can be managed by using techniques such as fairness-aware machine learning algorithms, data augmentation, and regularization.

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

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How would you define AI ethics in your own words?

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How do beneficence and non-maleficence apply to AI ethics?

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How can AI companies address societal fears about automation?

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Explain the difference between data bias and algorithmic bias.

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

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How can anomaly detection systems improve AI safety?

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What are the societal benefits of explainable AI?

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How can preprocessing techniques reduce bias in datasets?

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What steps can be taken to secure user data in AI systems?

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

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How do you balance explainability and model performance?

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