AI Transparency & Explainability

"The computer said so" is no longer a valid legal defense. You must be able to explain why an AI model made a specific decision.

The "Black Box" Problem

Deep learning models are opaque. They have billions of parameters, making it impossible for a human to trace the logic flow. This creates a "Black Box" risk.

Explainable AI (XAI) Techniques

1. Feature Attribution (SHAP/LIME)

For classical ML models, we use SHAP (SHapley Additive exPlanations) values to show which input features contributed most to the output.

Example: "The loan was denied because 'Debt-to-Income Ratio' contributed -40 points."

2. Chain-of-Thought (CoT)

For LLMs, we force the model to "show its work." By prompting the model to "Think step-by-step," we generate a natural language explanation of the reasoning process.

3. Model Cards

Transparency isn't just about individual decisions; it's about the model itself. A "Model Card" is like a nutrition label for AI, documenting:

• Intended Use Cases
• Training Data Sources
• Known Limitations & Biases
• Performance Metrics

Regulatory Requirements

The EU AI Act and GDPR (Article 22) mandate a "Right to Explanation" for automated decisions that significantly affect individuals.