Why is model explainability important While the model in the example may have been safe and accurate, the target users did not trust the AI system because they didn’t know how it made decisions. Sep 29, 2022 · Explainability is the capacity to express why an AI system reached a particular decision, recommendation, or prediction. Also, if it is possible to learn a highly accurate surrogate model, we should question why we are not using an interpretable model to begin with. Explainability is often unnecessary. Semantic models allow stakeholders to understand the reasoning behind forecasts or classifications, increasing confidence in automated systems. For instance, the above model was made explainable using a simple visualization. It's best to understand why a model works in order for you to trust it. Greater interpretability requires greater disclosure of its internal operations. Mar 16, 2021 · A Stanford researcher advocates for clarity about the different types of interpretability and the contexts in which it is useful. It makes models transparent and solves the black box problem. mal tsjmbv cznty rgij ftukw phchpf fydcjj giy qzqjmy kfaxgwd