How Can AI Models Be Made Interpretable and Explainable?

Akanksha23 Dec, 2024Education

Use inherently interpretable models like decision trees, linear regression, or logistic regression for applications where transparency is critical. Simplify model architecture by limiting the number of features or layers (for neural networks), even if it results in a slight reduction in accuracy. techniques like Grad-CAM (Gradient-weighted Class Activation Mapping) highlight regions of the input that influence predictions.