explainable

Explainable describes the quality of being able to be explained or understood. In everyday use, something explainable can be clarified or justified in a way that a person can grasp. In technical contexts, explainability often refers to the ability to provide clear reasons, criteria, or processes behind a decision, action, or outcome.

In the field of artificial intelligence and machine learning, explainable AI (XAI) strives to make model predictions and behaviors understandable to humans. It distinguishes between interpretability (how easily a person can comprehend the model) and explainability (the provision of reasons or explanations for specific outputs). There are intrinsically interpretable models, such as decision trees or linear models, and there are post hoc explanations for complex models like neural networks. Common approaches include local explanations that justify individual predictions (for example, feature-attribution methods) and global explanations that describe overall model behavior. Techniques such as surrogate models, LIME, SHAP, counterfactual explanations, and visualization tools are widely used.

Explainability involves trade-offs among fidelity, usefulness, and simplicity. Explanations should faithfully reflect the model’s reasoning (fidelity) while remaining understandable to the intended user. Evaluating explanations poses challenges, including how to measure usefulness, accuracy, and potential misinterpretation. Practical considerations include privacy, data bias, and the risk of over-simplified or misleading explanations.

Applications span regulated sectors like finance, healthcare, and public policy, where accountability and trust are important, as well as general risk management for automated decision systems. Regulatory and standards efforts—across regions and organizations—address transparency, governance, and the responsible deployment of explainable methods.