What is model interpretability in AI?

What is model interpretability in AI?

Model interpretability refers to the degree to which a human can understand the cause of a decision made by a machine learning model.

Why is it important to have interpretable models?

Interpretable models are crucial for trust, accountability, and compliance, especially in sensitive areas like healthcare and finance.

Can you give examples of interpretable models?

Sure! Examples include linear regression, decision trees, and rule-based models, which are easier to understand than complex models.

What are some challenges in achieving model interpretability?

Challenges include balancing accuracy with interpretability, the complexity of models, and the lack of standardized metrics for evaluation.

How can I improve the interpretability of my AI model?

You can use techniques like feature importance analysis, LIME, SHAP values, or opt for simpler models that are inherently interpretable.