Explainable AI is making black box models worthless in the agentic era

Transparency and explainability are only way organizations can trust autonomous AI.
ascopubs.org

Comparative Evaluation of Explainable Machine Learning Versus Linear Regression for Predicting County-Level Lung Cancer Mortality Rate in the United States

This study applied three models—random forest (RF), gradient boosting regression (GBR), and linear regression (LR)—to predict county-level LC mortality rates across the United States. Model ...
Digital Journal

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

In an era where AI adoption frequently outpaces regulatory readiness, Archana Pattabhi, Senior Vice President at a leading global bank, led a forward-looking transformation that redefined how ...
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 ...
Medical Xpress

AI model offers accurate and explainable insights to support autism assessment

Scientists have developed and tested a deep-learning model that could support clinicians by providing accurate results and clear, explainable insights—including a model-estimated probability score for ...