Definition #
Explainable AI (XAI) refers to artificial intelligence systems that provide human-interpretable reasons for their outputs, enabling users to understand, trust, and manage AI decisions.
Key Characteristics #
- Model transparency features
- Local interpretable model-agnostic explanations (LIME)
- SHAP (SHapley Additive exPlanations) values
- Regulatory compliance (GDPR, AI Act)
Why It Matters #
Critical for high-stakes domains like healthcare and finance where understanding AI decisions is legally and ethically required. Reduces “black box” concerns.
Common Use Cases #
- Credit approval systems
- Medical diagnosis support
- Fraud detection explanations
Examples #
- LIME Python library
- IBM Watson OpenScale
- TensorFlow’s What-If Tool
FAQs #
Q: Does XAI reduce model accuracy?
A: Modern methods like SHAP provide explanations without compromising performance in most cases.
Q: Is XAI required by law?
A: The EU AI Act mandates XAI for high-risk AI systems starting 2026.