Why is explainability important in AI systems?
If you can?t explain it, you don?t really understand it.
How It Works:
Explainability tools (like SHAP or LIME) trace model decisions back to input features, helping humans understand why predictions occur.
Key Benefits:
- Trust building: Users see ?why? behind results.
- Error detection: Spot biases or data issues.
- Regulatory compliance: Meets transparency mandates.
Real-World Use Cases:
- Loan approvals: Justify credit decisions to applicants.
- Healthcare diagnostics: Clinicians validate AI recommendations.
FAQs
Are explanations always accurate?
Do they slow down inference?
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