Researchers propose a Holistic Explainable AI framework that embeds explanation into every stage of the data-analysis ...

When machine learning models deliver problematic results, it can often happen in ways that humans can't make sense of, and this becomes dangerous when there are no limitations of the model, ...

A leakage-aware evaluation of explainable machine learning found modest prediction of nanoparticle tumor delivery, with errors increasing for unseen materials and little transportability across ...

AI systems have tremendous potential, but the average user has little visibility and knowledge on how the machines make their decisions. AI explainability can build trust and further push the ...

Lung cancer (LC) is a leading cause of cancer-related mortality in the United States. Accurate prediction of LC mortality rates is crucial for guiding targeted interventions and addressing health ...

Using a real-world, nationwide electronic health record–derived deidentified database of 38,048 patients with advanced NSCLC, we trained binary prediction algorithms to predict likelihood of 12-month ...

Contribute to Praveshika2k/Graph-based-cryptocurrency-fraud-detection-with-explainable-machine-learning development by creating an account on GitHub.

This course explores the field of Explainable AI (XAI), focusing on techniques to make complex machine learning models more transparent and interpretable. Students will learn about the need for XAI, ...

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 ...

Fraud prevention combines rules, AI and behavioral analytics to stop scams, reduce false declines and keep automated ...