What is model interpretability in AI and why is it important?

What is model interpretability in AI and why is it important?

Model interpretability refers to the degree to which a human can understand the cause of a decision made by a machine learning model. It's important because it helps build trust, ensures accountability, and allows for better debugging of models.

Can you give me examples of interpretable models?

Sure! Examples of interpretable models include linear regression, decision trees, and rule-based models. These models provide clear insights into how input features influence predictions.

What are the challenges of achieving model interpretability?

Challenges include the complexity of models, such as deep learning, which can be difficult to interpret. Additionally, there is often a trade-off between accuracy and interpretability.

How can I improve the interpretability of my complex models?

You can use techniques like LIME or SHAP, which provide explanations for individual predictions, or consider simpler models that are inherently more interpretable.