Feature Attribution in AI Explainer
Feature attribution methods assign a contribution score to each part of an input for a particular model output. Features might be pixels, words, measurements, or engineered variables depending on the application.
Different methods use gradients, input perturbations, cooperative-game ideas, surrogate models, or comparisons with a baseline. Their scores answer related but not identical questions, so explanations can disagree even for the same prediction.
Attributions can help inspect whether a model relies on relevant evidence, compare cases, or guide debugging. They do not automatically describe causation, reveal the complete decision process, or show how changing a feature in the real world would affect an outcome.
Responsible use includes documenting the method and baseline, checking stability, testing faithfulness, and comparing with known controls. High-stakes decisions should not rely on a visually persuasive attribution alone.
Feature attribution assigns scores to input features to estimate how each one contributed to a particular model prediction.
Key terms include feature, attribution score, prediction, gradient, perturbation, baseline, faithfulness, stability and causation.
A method defines how absence, change, or contribution is measured.
Different definitions can produce different rankings of the same features.
An attribution describes model behavior under a method's assumptions.
It does not prove that changing the real-world feature will cause the predicted outcome to change.
Explore more "Explainers"
Discover additional explainers across politics, science, business, technology, and other fields. Each explainer breaks down a complex idea into clear, everyday language—helping you better understand how major concepts, systems, and debates shape the world around us.