AI Ethics
Explainability
The ability to understand and interpret how an AI model makes its decisions.
In-depth explanation
Explainability (or interpretability) is crucial for trust, debugging, and compliance. Methods include feature importance, attention visualization, LIME, SHAP, and concept activation vectors. There's often a trade-off between accuracy and explainability-simpler models are more interpretable but less powerful. Regulations increasingly require explainability in high-stakes decisions.
Examples
Explaining loan rejections
Medical diagnosis reasoning
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