How do I ensure Generative AI model interpretability for compliance applications where understanding the model s decision-making is critical

Question

With the help of proper code example can you tell me How do I ensure Generative AI model interpretability for compliance applications, where understanding the model's decision-making is critical?

Answer 1

Ensuring Generative AI interpretability for compliance applications involves integrating explainable AI (XAI) techniques, rule-based constraints, and transparency mechanisms to justify decision-making.

Here is the code snippet you can refer to:

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

• Explainable AI (XAI) Integration – Uses methods like LIME, SHAP, and rule-based constraints to justify decisions.
• Transparency in Decision-Making – AI documents and explains why certain compliance flags are raised.
• Auditable AI Outputs – Ensures regulatory compliance by maintaining logs of AI-driven decisions.
• Bias Detection & Mitigation – Identifies potential biases in AI-generated risk assessments.
• Human-AI Collaboration – Allows manual overrides and expert validation for high-stakes decisions.

Hence, integrating explainable AI techniques with Generative AI ensures transparent, auditable, and justifiable decision-making for compliance applications, enhancing trust, accountability, and regulatory alignment.