Explainable AI (XAI)
AI systems designed to make their functioning transparent and their decisions interpretable by humans.
Detailed Explanation
Explainable AI refers to methods and techniques that allow humans to understand and trust the results and output created by machine learning algorithms. XAI is crucial in applications where transparency is essential, such as healthcare, finance, and legal systems. The goal is to create AI systems whose actions can be understood by humans, rather than functioning as "black boxes." Techniques include using inherently interpretable models, extracting post-hoc explanations from complex models, visualizing neural network activations, and generating natural language explanations. XAI addresses both technical challenges (how to create interpretable models without sacrificing performance) and human factors (what kinds of explanations are most useful to different stakeholders).
Examples
- LIME and SHAP for feature importance
- Attention visualization in neural networks
- Decision trees for interpretable classification