Introduction to Explainable AI (XAI)
Explainable AI (XAI) refers to the subset of artificial intelligence focused on making the decisions and predictions of AI models understandable and interpretable to humans. As AI systems grow in complexity, particularly with the use of deep learning, their “black-box” nature poses challenges in trust, accountability, and regulatory compliance. XAI techniques aim to bridge this gap by providing insights into how AI models make decisions.
Source: Internet
Key Components of XAI
Model Interpretability:
- Ability to understand the inner workings of an AI model.
- Examples: Decision trees, linear regression, and simple neural networks are inherently interpretable.
Post-Hoc Explanations:
- Techniques that explain the decisions of black-box models without altering their architecture.
- Examples: LIME (Local Interpretable Model-Agnostic Explanations), SHAP (SHapley Additive exPlanations).