AI Transparency & Explainability: Manager’s Guide | Quiz

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AI Transparency & Explainability Manager's Guide

AI Transparency & Explainability: Manager’s Guide | Quiz

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1. Why is attention visualization for LLMs considered limited as an explanation technique?

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2. According to the EU AI Act what level of transparency is required for high-risk AI systems?

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3. What makes glass-box models like EBMs and GAMs valuable for high-stakes decisions?

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4. What is the key limitation of post-hoc explanation methods like LIME and SHAP?

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5. A credit card company receives complaints about different credit limits for spouses with similar profiles. Without explainability capability what is the primary challenge they face?

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6. What does SHAP use to determine feature importance in AI predictions?

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7. Why is the accuracy-explainability trade-off a genuine business challenge for managers?

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