Interpretability
term_id: interpretability
Category: ethics_safety
Definition
Interpretability, or explainability, involves making the internal workings and decision-making processes of AI models transparent and understandable to humans. This is crucial for debugging, ensuring fairness, and building trust in high-stakes applications. Techniques include feature importance analysis, SHAP values, and attention visualization. Unlike black-box models, interpretable systems allow stakeholders to audit decisions, identify biases, and verify that the model relies on relevant features rather than spurious correlations.
Summary
The degree to which a human can understand the cause of a decision made by an AI model.
Key Concepts
- Explainable AI (XAI)
- Transparency
- Feature Importance
- Trust
Use Cases
- Loan approval reasoning
- Medical treatment recommendations
- Bias auditing in hiring algorithms