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General

Explainable AI (XAI)

As AI models become more complex (e.g., deep neural networks), they often behave as “black boxes.” Explainable AI (XAI) aims to make AI decisions more transparent and understandable to humans.

Why XAI?

  • Trust: Users are more likely to trust a system that explains its reasoning.
  • Accountability: Essential for high-stakes domains like medicine and law.
  • Debugging: Helps developers identify biases or errors in the model.

Common Techniques

  • LIME (Local Interpretable Model-agnostic Explanations): Explains individual predictions.
  • SHAP (SHapley Additive exPlanations): Based on cooperative game theory.
  • Feature Importance: Visualizing which features contributed most to a decision.

Regulatory Impact

Regulations like GDPR include a “right to explanation,” making XAI a legal necessity for many businesses.