Working with XNNs
Practical interaction: how to query an XNN, how a prediction is assembled, and how rollups, rule views and attribution views expose the reasoning.
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01
How to Query XNNs and Receive Explanations
Querying an Explainable Neural Network (XNN) is an explainable inference workflow. The Explanation Structure Model (ESM) applies the recorded preprocessing, the XNN produces a prediction and module attributions, and the explainer assembles the rules, activations, views and metadata needed for the use case.
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02
How XNN Predictions are Built
Explainable Neural Network (XNN) predictions are additive before the final task-specific transformation. Each active module rule produces an attribution, those contributions are combined with a background value, and an identity, sigmoid or softmax function produces the model output.
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03
How Feature Rollups, Rule Views and Attribution Views Work
Explainable Neural Network (XNN) views answer different questions about the same execution. An attribution view shows contribution, a rule view shows the logic that activated, and a feature rollup brings direct and interaction effects back to the original inputs.
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04
How Local, Module-Level and Global Interpretability Work
Explainable Neural Network (XNN) interpretability operates at three connected scopes. Local views explain one prediction, module-level views examine one feature or interaction, and global views aggregate behaviour across many queries.