# Explainable AI > [!metadata]- Metadata > **Published:** [[2025-02-09|Feb 09, 2025]] > **Tags:** #🌐 #learning-in-public #artificial-intelligence #ethical-ai #bias-mitigation #cognitive-science #justice-system Explainable AI (XAI) refers to methods and techniques that make artificial intelligence systems' decision-making processes transparent and interpretable to humans. This is crucial for ensuring [[Algorithmic Bias|algorithmic fairness]] and maintaining trust in AI systems. ## Key Components 1. **Interpretability Methods**: - Model-specific explanations - Feature importance analysis - Decision path visualization - Counterfactual explanations 2. **Transparency Levels**: - Global interpretability (entire model behavior) - Local interpretability (individual predictions) - Process transparency (development lifecycle) ## Common Techniques 1. **LIME (Local Interpretable Model-agnostic Explanations)**: - Explains individual predictions - Works with any machine learning model - Creates locally faithful explanations 2. **SHAP (SHapley Additive exPlanations)**: - Based on game theory - Assigns feature importance values - Provides consistent explanations 3. **Attention Mechanisms**: - Shows which inputs are most influential - Particularly useful in deep learning - Visualizes model focus areas ## Applications 1. **Healthcare**: - Explaining diagnostic decisions - Treatment recommendations - Risk assessments 2. **Financial Services**: - Loan approval explanations - Fraud detection reasoning - Investment recommendations 3. **Legal Requirements**: - Regulatory compliance - Right to explanation - Audit requirements ## Challenges 1. **Technical Limitations**: - Trade-off between accuracy and interpretability - Computational overhead - Model complexity 2. **Implementation Issues**: - Integration with existing systems - Performance impact - Resource requirements ## Relationship to Ethical AI Explainable AI supports: - [[Fairness Definitions|Fair decision-making]] - [[Bias Mitigation Techniques|Bias detection and mitigation]] - Accountability in AI systems - Trust building with stakeholders [Learn more about explainable AI techniques](@https://arxiv.org/abs/2208.05126)