Explainable AI

Explainable AI encompasses the methods and practices that help people understand why an AI system produced a particular output. This can mean feature importance (which inputs drove the decision), confidence scores, counterfactual reasoning (what would have changed the outcome), or natural-language summaries of the model's logic. What constitutes adequate explanation depends on who's asking and why: a data scientist debugging a model needs different information than a customer contesting a loan denial or a regulator reviewing a compliance workflow. Explainability is a design requirement, not a setting.

Many AI systems are deployed with vendor assurances of transparency that don't hold up under scrutiny. A model that can produce a plausible-sounding explanation isn't necessarily providing a true one — post-hoc explanation methods approximate model behavior, they don't reveal ground truth. In regulated contexts — lending, employment, healthcare — explanation isn't optional; it's legally required, and the required form is specific. Using an inherently opaque model architecture where explanation is mandated creates a structural compliance gap that no vendor claim can close.