🔍 Explainable AI (XAI)
Understand and interpret AI model decisions with transparency and trust
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0 / 5 completedIntroduction to Explainable AI
🎯 What is XAI?
Explainable AI (XAI) refers to methods and techniques that make the behavior and predictions of machine learning models understandable to humans. It addresses the "black box" problem by providing insights into how models arrive at their decisions.
Trust, accountability, and regulatory compliance require understanding AI decisions.
🤔 Why XAI Matters
Healthcare
Doctors need to understand why AI recommends a treatment or diagnosis
Finance
Banks must explain loan rejections and credit decisions to customers
Legal Compliance
GDPR's "right to explanation" requires transparency in automated decisions
Debugging
Identify model errors, biases, and improve performance
📊 Interpretability Spectrum
Intrinsically Interpretable
High TransparencyModels where the entire decision process is understandable
Post-hoc Explainable
External MethodsComplex models requiring external explanation techniques
🎭 Types of Explanations
Global Explanations
Overall model behavior - which features are generally important
Local Explanations
Why a specific prediction was made for a particular instance
Example-Based
Show similar examples that influenced the prediction
Counterfactual
What would need to change for a different prediction
⚖️ Accuracy vs Interpretability Trade-off
Complex models often achieve higher accuracy but sacrifice interpretability