How can we enhance the interpretability and explainability of AI models to build trust and facilitate human understanding?

Simpler Model Architectures: Use simpler model architectures that are easier to understand and interpret, such as decision trees, linear models, or rule-based systems. These models often have transparent decision-making processes that can be easily explained to non-experts.

1. Feature Importance Analysis: Conduct feature importance analysis to identify which input features have the most significant impact on the model's predictions. Techniques such as permutation importance, SHAP values, or LIME can help highlight the contribution of individual features to the model's decisions.




2. Visualization …

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