How do I implement model explainability in Generative AI for content generation tasks to improve trust in AI systems

Question

Can you tell me How do I implement model explainability in Generative AI for content generation tasks to improve trust in AI systems?

Answer 1

You can use SHAP or LIME to interpret token-level contributions in generative AI models, improving transparency and trust in AI-generated content.

Here is the code snippet you can refer to:

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

• SHAP Explainability Integration – Uses SHAP to analyze token contributions in text generation.
• Token-Level Contribution Analysis – Identifies which words influence AI-generated content most.
• GPT-2 Model Interpretation – Provides transparency into how language models form responses.
• Visualization of SHAP Values – Displays token importance to help users understand AI decisions.
• Improved Trust in AI Systems – Enhances explainability and reduces AI bias concerns.

Hence, incorporating SHAP or LIME in generative AI models enhances trust by offering interpretable insights into content generation decisions.