AI Explainability Statement Generator
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Common Compliance Questions
Q: What is AI explainability?
The ability to explain in human-readable terms why an AI model reached a specific decision.
Q: What is explainable AI (XAI)?
Models designed to provide clear reasoning paths alongside their outputs.
Q: Why is explainability important for compliance?
Under GDPR, users have a right to explanation for automated choices that affect them.
Q: What is the difference between interpretable and explainable AI?
Interpretable models (like decision trees) are simple enough to understand directly; explainable models (like deep nets) need secondary tools to explain outputs.
Q: What are SHAP and LIME values?
Mathematical tools used to explain deep learning decisions by showing which inputs mattered most.
Q: Does explainability reduce model accuracy?
Sometimes, as simpler, explainable models might perform slightly worse than complex black-box nets.
Q: Should explainability reports be public?
Summary reports should be public, while detailed feature logs should be kept for internal audits.
Q: How does explainability prevent discrimination?
It reveals if the model is relying on protected data (like race or gender) to make choices.