Forbes

How Explainable AI Can Boost Customer Retention

Fighting churn isn't about predicting it alone. It also involves understanding the hidden factors that lead to churn and how to drive customer loyalty. Although traditional churn prediction models may ...
Nature

Explainable machine learning by SEE-Net: closing the gap between interpretable models and DNNs

Deep Neural Networks (DNNs) have achieved remarkable accuracy for numerous applications, yet their complexity often renders the explanation of predictions a challenging task. This complexity contrasts ...
Nature

Development and explainable AI-driven characterization of a prognostic model for haploidentical transplantation outcomes

We developed a novel, explainable artificial intelligence (AI)-driven prognostic model using a contemporary single-centre cohort of 668 patients undergoing haploidentical hematopoietic cell ...
Forbes

AI Bridges The Trust Gap: How Explainable AI Aligns Modelers And Business Leaders

Artificial intelligence (AI) continues to transform industries—from finance and healthcare to marketing and logistics. Yet one persistent challenge remains: trust. Many organizations see AI models as ...
Digital Journal

Pioneering explainable AI in banking: How Archana Pattabhi transformed model governance at scale

Opinions expressed by Digital Journal contributors are their own. In an era where AI adoption frequently outpaces regulatory readiness, Archana Pattabhi, Senior Vice President at a leading global bank ...
ascopubs.org

Preoperative Maximum Standardized Uptake Value Emphasized in Explainable Machine Learning Model for Predicting the Risk of Recurrence in Resected Non–Small Cell Lung Cancer

This retrospective study included 643 patients who had undergone NSCLC resection. ML models (random forest, gradient boosting, extreme gradient boosting, and AdaBoost) and a random survival forest ...