Introduction
Transparency is critical for building trustworthy AI systems. It means understanding how your model arrives at its decisions and being able to explain those decisions to stakeholders. This guide covers key techniques and best practices.
This tutorial focuses on the crucial concept of model explainability.
What is Transparency?
Beyond simply providing output, transparency necessitates:
- **Model Interpretability:** The ability to understand the model's inner workings.
- Explainability:** Providing clear reasoning behind predictions.
- Auditability:** The ability to trace and review model behavior.
- Bias Detection:** Identifying potential biases embedded within the model.
Key Techniques
Several techniques are employed:
- SHAP Values: Explain individual feature contributions.
- LIME (Local Interpretable Model-agnostic Explanations): Explain model behavior locally.
- Attention Mechanisms: Visualize which parts of the input the model focuses on.
- Counterfactual Explanations: Show what changes to the input would lead to a different prediction.