Explanations and Interpretability
What makes an explanation useful, how different questions require different forms of explanation, and how XNN attributions connect model behaviour to features and interactions.
- Read article
01
What Makes a Good AI Explanation?
A useful AI explanation must reflect what the model actually did, contain enough information for its purpose and remain traceable to the relevant inputs and model components. It must also be presented at the right level for the person using it.
- Read article
02
The Different Types of Explanations in Hybrid Intelligence
"Explain this result" can mean several different things. Hybrid Intelligence distinguishes seven explanation types so that the system answers what the reader actually asked, rather than returning the same feature-importance chart for every situation.
- Read article
03
How XNN Attributions Explain Decisions
An Explainable Neural Network (XNN) attribution is a signed contribution produced by an active module rule. Attributions build the prediction and the explanation together: they show how much each feature or interaction pushed the model output up or down.
- Read article
04
How to Interpret XNN Results
An Explainable Neural Network (XNN) result contains more than a label or number. A disciplined reading starts with the task and output scale, reconciles the background value and attributions, inspects the active rules, and then considers activation context and verification metadata.
- Read article
05
How XNNs Support What-If and Counterfactual Analysis
Explainable Neural Networks (XNNs) can rerun the same explicit model logic under controlled changes. What-if analysis tests a change chosen by the user; counterfactual analysis searches for a feasible route to a target; what-if-not analysis isolates the effect of removing or replacing a factor.