AI Interpretability Lab: Decoding Neural Network Decision-Making

Feature Attribution

Feature attribution is a technique used to understand which input features contribute most significantly to the model’s predictions. By analyzing these attributions, we can gain insights into what aspects of the data are driving the model's decisions.

In the context of our AI Interpretability Lab, feature importance is calculated by assessing how much each input feature contributes to the final prediction across all instances in the dataset.

Saliency Mapping

Saliency mapping focuses on identifying which parts of an input are most influential for a given model output. This is often visualized as heatmaps that highlight regions of interest within images or text inputs.

By applying saliency methods in our lab, we can see how the neural network prioritizes different features to make its predictions.

Attention Flow

Attention flow visualization provides a dynamic view of how the model allocates attention across input features during decision-making. This method is particularly useful for understanding the sequential processing in models like transformers.

In our lab, you can observe how the network’s attention shifts and changes as it processes different parts of an input, offering insights into its reasoning process.

Why It Matters

Understanding these methods is essential for developing more transparent AI systems that can be trusted in critical applications such as healthcare, finance, and autonomous vehicles.

Moreover, interpretability techniques help in debugging models, improving their performance, and ensuring they adhere to ethical standards.

Frequently asked questions

Try it live

Everything above runs in your browser — open AI Interpretability Lab: Feature Attribution, Saliency & Attention Explorer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.