Model Interpretability and Explainability

The Core Idea

Deep learning relies on representing data across layered feature spaces.

Understanding how a model makes decisions is crucial for building trust and ensuring responsible AI development.

(a) Model-Agnostic Methods: These methods can be applied regardless of

LIME (Local Interpretable Model-Agnostic Explanations) works by perturbing the input data around a specific instance and fitting a simple, interpretable model to these local perturbations.

The coefficients of this linear model represent the local feature importance. This allows us to see which features had the biggest impact on that particular prediction.

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Future trends shaping the evolution of XAI technologies.

SHAP (SHapley Additive Explanations) leverages concepts from cooperative game theory to assign each feature an ‘importance value’ - a Shapley value.

This represents the feature's contribution to the prediction, considering all possible combinations of features. SHAP offers several variants tailored for different model types.

Frequently asked questions

What is Model Interpretability and Explainability?

Model interpretability and explainability refer to the ability to understand how a machine learning model makes decisions. This helps us identify potential biases, debug errors, and build trust in AI systems.

What is LIME?

LIME stands for Local Interpretable Model-Agnostic Explanations. It's a technique that explains individual predictions by creating a simple, interpretable model around the specific data point used to make the prediction.

What is SHAP?

SHAP stands for SHapley Additive Explanations. It uses game theory to fairly distribute credit for a prediction among all features, offering a more comprehensive understanding of feature importance than simpler methods.

▶ Try it live

Everything above runs in your browser — open Gradient Descent Visualiser and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Gradient Descent Visualiser simulation