Instruction: Discuss how explainable AI practices can either mitigate or exacerbate biases in AI models.
Context: This question aims to explore the candidate's understanding of the relationship between AI Explainability and algorithmic fairness, including the potential ethical implications.
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The way I'd explain it in an interview is this: Explainability improves fairness work by making model behavior easier to inspect. It can help teams identify proxy features, segment-level failure patterns, unstable decision logic, and discrepancies between how the model behaves overall versus for specific groups.
But I would...