How can I make my generative AI models more explainable when deployed in regulated industries like healthcare or finance

Question

With the help of code can you tell me How can I make my generative AI models more explainable when deployed in regulated industries like healthcare or finance?

Answer 1

To make generative AI models more explainable in regulated industries like healthcare or finance, use attention visualization, model interpretability techniques (e.g., SHAP, LIME), and rule-based constraints to ensure transparency and accountability.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• SHAP Interpretability – Uses SHAP (SHapley Additive Explanations) to provide insights into model predictions.
• Token-Level Importance – Identifies which words contribute most to the model's output.
• Regulatory Compliance – Helps justify AI decisions in healthcare and finance by making reasoning transparent.
• Model-Agnostic Approach – SHAP can be applied to various generative models beyond GPT-2.
• Visualization Support – Provides interpretable visual plots to explain AI-generated text.

Hence, ensuring explainability in generative AI for regulated industries requires interpretability techniques like SHAP, attention visualization, and rule-based constraints to enhance trust, transparency, and compliance.